Athina Ai Software Engineer Interview Guide

The 60-second brief

Athina Ai builds tooling for teams that run large language model applications in production. Candidate reports describe the product as covering the monitoring, evaluation and security of those applications, so the data the platform handles is log, telemetry and evaluation-metric data produced by other companies' LLM apps. The Software Engineer role is described as spanning backend services that process that data at volume, integrations with third-party model providers such as OpenAI and Anthropic, and user interfaces that present the results to developers. Athina Ai is described as an early-stage company, and the topics attached to this role are GenAI Product Development, Model Evaluation, Node.js, TypeScript and Python. Reports also say the engineer works directly with the founding team and product leaders to turn customer feedback into engineering work.

This guide is for engineers interviewing for a Software Engineer position at Athina Ai. Reports describe the typical candidate as mid-to-senior with several years of production experience, though they say strong projects can offset years. It covers the four reported rounds, the system design, past-experience and startup-fit questions candidates report, behavioral stories, and a seven-day plan.

Candidates report four rounds over about 3-5 weeks: an Exploratory Screen, a Technical Discussion, a Leadership Discussion that includes the CTO, and a Final Evaluation. Reports describe the conversations as informal in tone and centered on past projects and architecture rather than puzzles.

Focus areas

• GenAI Product Development
• Node.js
• Model Evaluation (GenAI Evaluation)

Overview

The reported role is a backend-heavy, full-stack-adjacent job: services that ingest and aggregate evaluation and log data, APIs that sit in front of external LLM providers, a dashboard on top, and monitoring of the platform itself. The five system design questions follow that shape: a real-time dashboard over millions of LLM logs, a schema for a user-facing analytics application, SQL versus NoSQL for unstructured AI telemetry, availability and latency in a distributed backend, and rate limiting for a public API. Prepare them as one connected system (ingest, store, query, protect) rather than five unrelated answers, and be ready to say which store, index or queue you would pick and what query or failure it makes harder.

Five more questions ask about your own work: owning a feature end to end, optimizing a slow application, a production mistake, how you decide what to test, and a disagreement with a peer or manager. Candidates describe the rounds as conversational, so there may be no problem statement to anchor on; your past projects are the material. Build a story bank of four or five projects, each with your role, the stack, one trade-off, one failure or surprise, and one result you can defend with a number or an observable outcome. One strong story can usually be bent to answer several of these prompts.

The last group is startup fit: why an early-stage company in AI observability, how to prioritize when several features are labelled critical, how to balance technical debt against shipping, how to build with little direction, and how you keep up with AI tooling. The listed requirements center on Python, Node.js or Go, PostgreSQL and REST API design, with React, LLM provider APIs and observability tools as extras. You do not need LLM experience to answer well, but you should understand the general problem of observing an LLM application (logging prompts and responses, tracking latency and cost, scoring outputs) so that your design answers and your motivation answer sound informed.

The Athina Ai interview process

Candidates report four rounds, in the order below, over about 3-5 weeks, and say the sequence can shift with seniority and location. No report ties a specific question to a specific round, so the preparation below is organized by question type.

Exploratory Screen

Candidates describe the first stage as an initial conversation about your background and engineering philosophy. Nothing more specific is reported about its format, so prepare to talk rather than to solve: a spoken account of your career that points at the projects and decisions you want to be asked about. Since later rounds are reported as conversational too, this is where you plant the stories you will expand on later.

What to demonstrate

• How clearly you summarize your background and engineering philosophy
• Whether your past work connects to building and operating a product for LLM applications
• Your reasons for wanting an early-stage company in this space

How to prepare

• Write a spoken career summary built around two projects, each with the trade-off you made and a result you can measure.
• Prepare your answer to why you want an early-stage startup in AI observability, tied to something you have actually built or run.
• Write two or three questions of your own about the engineering team's stack and scaling problems.

Technical Discussion

Candidates describe an in-depth discussion of your technical depth and past projects. They also report that the process favors architecture and real-world systems over algorithm puzzles, so expect to explain designs you built and to reason through designs you have not. The system design questions candidates report (a dashboard over millions of LLM logs, a schema for an analytics application, SQL versus NoSQL for unstructured telemetry, availability and latency, rate limiting) and the past-experience questions are the material to rehearse for a discussion like this.

What to demonstrate

• Depth of knowledge in the systems on your resume, including why you chose them
• Reasoning aloud about storage, latency and reliability trade-offs
• A clear account of what you personally contributed to past projects

How to prepare

• Pick two projects and practice drawing their architecture from memory: components, data stores, failure modes, and what you would change now.
• Rehearse the dashboard-over-LLM-logs design aloud: ingestion, storage, rollups, API, and which numbers are allowed to be stale.
• Prepare a measured performance story with baseline, hypothesis, change and re-measurement, plus your testing philosophy.

Leadership Discussion

Candidates describe a casual yet professional chat with key decision-makers, including the CTO, and say it can feel like a collaborative conversation about your background and engineering philosophy rather than a technical interrogation. The tip they pass on is not to wait for follow-up questions: answer broad prompts with a structured story (Situation, Task, Action, Result) and move into technical specifics yourself.

What to demonstrate

• Ability to explain technical decisions clearly to senior, non-specialist listeners
• Structure and concision when answering open-ended questions
• How your technical choices connect to user and business outcomes

How to prepare

• Turn your best end-to-end ownership story into a structured account: a technical hurdle, the decision, the outcome.
• Practice the rate-limiting and availability answers in plain language: what breaks, who notices, what you do about it.
• Prepare a real example of prioritizing when everything was called urgent, with what you dropped and why.

Final Evaluation

Candidates describe the last stage as final discussions about fit and alignment with company culture. No technical format is reported for it, so prepare for the startup-fit questions candidates report: why an early-stage AI observability company, how you prioritize when several features are called critical, how you balance technical debt against shipping speed, and how you stay current with AI tooling. Bring your own questions as well, since candidates say they matter in conversational interviews.

What to demonstrate

• Motivation for joining an early-stage company in AI observability
• Approach to prioritization, technical debt and shipping speed
• Comfort with ambiguity, shown through concrete examples

How to prepare

• Write a short prioritization rule you actually use (user impact, risk, cost of delay) and apply it to a real past conflict.
• Prepare a technical-debt policy with an example of debt you took on deliberately and one you paid down.
• Write questions about the team's tech stack, biggest scaling challenges and direction in AI observability.

Where candidates lose points

Giving a one-sentence answer to a broad opening question and waiting for the next prompt

Candidates describe conversational rounds where you are expected to steer. Answer broad questions in three parts: a claim, one concrete example from your work, and the trade-off you accepted. Rehearse this on the end-to-end ownership and slow-application questions.

Naming a database before stating the access pattern in the LLM-log dashboard and schema questions

Begin with write rate, the filters the dashboard needs (time range, application, model, status) and acceptable staleness. Then choose storage, indexes and rollups, and say what query each choice makes expensive.

Treating rate limiting as the name of an algorithm, such as 'token bucket', and stopping there

Cover the key you limit on (API key, user, IP), where the counters live so several instances agree, what the client receives when rejected, and how a client or your own service backs off when an external LLM provider rejects calls.

Telling project stories in the plural with no measurements, so your own decisions are invisible

For each story, write down the decision you made, the alternative you rejected, and one number or observable result. Say 'I' for your decisions and 'the team' only for what the team did.

Answering the startup-fit questions with slogans like 'I like fast-paced environments'

Tie why an early-stage AI observability company to something you have built, such as logging, evaluation or production monitoring. Back the prioritization and technical-debt answers with one real example each, including what you dropped.

Questions & practice

Canonicalise a request body into a stable idempotency fingerprint

Medium · parsing · canonicalisation · hashing

idempotency_key.request_fingerprint is a SHA-256 over the method, path and canonicalised body, and a retry whose fingerprint differs must be rejected with 422 rather than served the stored response. Write the canonicaliser. Bodies are JSON up to 256 KB nested at most 32 levels; clients vary key order, whitespace and unicode escaping, and some send 64-bit ids as JSON numbers. Produce a deterministic byte string such that semantically identical bodies match and any semantic difference does not. State your complexity and name two normalisations you refuse to perform.

Approach

1. Parse once into a tree, then re-serialise under fixed rules: object keys sorted, array order preserved, one escaping convention, no insignificant whitespace. Parsing is O(n) and sorting keys is O(k log k) per object, so O(n log n) overall with O(depth) stack, and the 32-level cap is enforced during parsing because hostile nesting is how a canonicaliser becomes a stack overflow.
2. Sort keys by their UTF-8 bytes and say why the obvious implementation is wrong in some runtimes: a default string comparison that orders by UTF-16 code units places surrogate pairs, meaning code points from U+10000 up, below U+E000 to U+FFFF, which is not UTF-8 byte order, so two services written in different languages disagree on the same document.
3. Do not re-encode numbers through a double. IEEE-754 binary64 represents integers exactly only up to 2^53, so normalising a 19-digit id through a float changes it, and 1 against 1.0 cannot be reconciled without deciding whether they are the same value. Preserve the literal token, and require ids as strings at the API boundary if you want them comparable.
4. Reject duplicate keys rather than picking one. JSON permits them and parsers disagree, most keeping the last, so any choice you make ties the fingerprint to a parser detail that the code handling the request does not necessarily share.

Follow-up

• A client sends the same logical request with an extra field your API ignores. Same key, different fingerprint, so you return 422. Is that the right answer?
• Where does the fingerprint get computed relative to request decompression and the body-size limit?

Collapse a redelivered event batch into per-aggregate high-water marks

Easy · hashing · at-least-once · aggregation

You drain a batch of up to 5,000,000 events, each (aggregate_id BIGINT, aggregate_version INT, event_type, payload). The log guarantees order within one aggregate only; the batch merges 64 partitions, and a relay failover has redelivered a range, so an older version for an aggregate can appear after a newer one. Given a map of last_applied_version per aggregate, produce the events worth applying, at most one per (aggregate_id, version), plus the count discarded. Target O(n) time. State the memory for 2,000,000 distinct aggregates and what you do when it does not fit.

Approach

1. One pass, one hash map from aggregate_id to the highest version kept, and a discard counter. An event whose version is at or below last_applied_version for its aggregate is dropped without further work, which is the whole reason the event carries its version rather than a delta. O(n) expected time, O(d) space in distinct aggregates.
2. Keep the maximum, never the last occurrence. The redelivered range means the final appearance of an aggregate in the batch can be an older version than one seen earlier in the same batch, so last-wins applies stale state over newer state and the projection regresses with no error anywhere.
3. Cost the memory instead of calling it large: an 8-byte key plus a 4-byte version is 12 bytes of payload, and an open-addressed table held at a 0.7 load factor costs roughly 17 bytes per entry before per-slot metadata, so 2,000,000 aggregates is tens of megabytes in a native layout and several times that in a runtime that boxes both key and value.
4. If the distinct set exceeds memory, partition on hash(aggregate_id) mod P and reduce each partition independently. Every event for one aggregate hashes to the same partition, so the per-partition result is exact and the merge is concatenation rather than a second reduction.

Follow-up

• The payload is a patch rather than a snapshot, so applying only the highest version loses the intermediate changes. What changes in your reduction?
• How do you detect that version 7 arrived while version 6 was never delivered, and what should the consumer do about the gap?

Archive a resource graph without breaking live references or recursing

Medium · graph traversal · topological order · tenant isolation

Resources reference other resources within a tenant; for the largest tenant the reference table holds up to 2,000,000 nodes and 8,000,000 edges. Archiving a resource must archive everything reachable from it that nothing outside the set still references, refuse when a live external referrer exists, and terminate when references form cycles, which they legitimately do. Produce the archive order and the refusal list, targeting O(V+E). Say what stops the traversal crossing a tenant boundary, and why recursion is the wrong control structure at this size.

Approach

1. Load the subgraph with the tenant predicate on both endpoints of the edge, not only on the side you started from. Scoping the left table alone is the classic cross-tenant leak: one mis-entered edge then pulls another tenant's resources into the traversal and, worse, into the archive.
2. Traverse iteratively with an explicit stack. A 2,000,000-node graph can hold a chain deep enough to exhaust a native stack in the low tens of thousands of frames, and that failure is a process crash rather than an error you can return.
3. Treat cycles as data rather than corruption: compute strongly connected components with Tarjan in O(V+E) using its own explicit stack, then condense. The condensation is a DAG, so a topological order over it gives the archive order, and every member of a component archives in one transaction because no order within a cycle is valid.
4. Decide refusals with reverse edges. A candidate is archivable only if every in-edge originates inside the candidate set, so build the transpose or count in-degrees restricted to the visited set, and emit each blocked resource with the id of the external referrer, which is the only part of the answer an operator can act on.

Follow-up

• The graph is read in one query and the archive writes a minute later. What can change in between, and how do you make the write safe?
• The candidate set is 400,000 resources. Is that one transaction, and if not, what does a half-finished archive look like to a reader?

Walk me through the database schema you would design for a user-facing analytics application.

Medium · Architecture & System Design

Approach

1. Choose a concrete product and split the schema in two. Entities: organizations, users, projects and api_keys, normalized with foreign keys. Events: one append-only fact table at one-row-per-request grain, for example request_id, project_id, created_at timestamptz, model, prompt_tokens, completion_tokens, latency_ms, status and cost.
2. Give child facts their own tables at their own grain, such as spans per request or eval_results (request_id, metric, score). If you join them to requests before aggregating, request-level sums like cost inflate. Aggregate each table at its own grain first, then join.
3. Index for the dashboard queries. A composite index on (project_id, created_at DESC) serves 'this project, this time range'. A BRIN index on created_at is cheap for an append-only table. Customer metadata goes in a JSONB column with a GIN index for @> containment lookups.
4. Scale and retention: range-partition the fact table by month so old data is removed with DROP or DETACH PARTITION instead of a large DELETE. Serve heavy charts from rollup tables or a materialized view. REFRESH MATERIALIZED VIEW CONCURRENTLY needs a unique index on the view.
5. Edge cases: store UTC timestamptz and bucket in the viewer's time zone, put tenant_id on every table (row-level security is an option), use keyset pagination instead of large OFFSETs, and decide upfront how deleting a user or project cascades to its events.

Follow-up

• Write the query for daily cost per model over the last 30 days for one project. Which index does it use?
• Customers want custom fields on each event. Would you use JSONB, an EAV table or added columns, and what does each choice cost at query time?
• How would you handle a request to erase all logs belonging to one end user?

What are the trade-offs between using a SQL versus a NoSQL database for storing unstructured AI telemetry data

Medium · Architecture & System Design

What are the trade-offs between using a SQL versus a NoSQL database for storing unstructured AI telemetry data?

Approach

1. Start by naming what 'unstructured' means here. Telemetry usually has a fixed envelope (timestamp, tenant, model, latency, tokens, status) plus a variable payload (prompt, response, tool calls, arbitrary metadata). Most queries filter and aggregate on the envelope.
2. Relational (PostgreSQL): transactions, joins to tenants and eval results, typed columns, and a JSONB payload indexed with GIN. Costs: a row store scans whole rows for aggregations, every index adds write cost, large values are TOASTed, and high write volume needs partitioning and eventually sharding or another store.
3. Document stores (e.g. MongoDB) give a flexible schema and built-in sharding. Wide-column stores (e.g. Cassandra) handle very high write throughput but need tables modelled around known queries and support little ad-hoc aggregation or joining. Either way you move from schema-on-write to schema-on-read, so every reader must handle missing or changed fields.
4. Columnar OLAP engines (e.g. ClickHouse) use SQL but are built for append-heavy analytics. Aggregations are fast; row-level updates and deletes are expensive. Point out that 'SQL vs NoSQL' is less useful than 'transactional vs analytical access pattern'.
5. Close with a hybrid answer and the deciding factors: relational for entities and the envelope (or OLAP once write volume demands it), and object storage for raw bodies. Then name what drives the choice: write rate, query shapes, retention, deletion requirements and what the team can operate.

Follow-up

• How do you make a lookup on one JSONB key fast, and how do a GIN index with jsonb_path_ops, a default GIN index and an expression index on that key differ?
• The payload format changes between SDK versions. How do readers cope in each option?
• How would you delete one customer's data on request in a relational store, a document store and a columnar store?

How would you design a dashboard that needs to display real-time analytics from millions of LLM logs?

Medium · Architecture & System Design

Approach

1. Pin down the metrics and freshness first: request volume, error rate, p50/p95/p99 latency, token usage and cost, sliced by tenant, model and prompt version. Agree on how fresh 'real-time' must be (seconds or a minute) and how far back users can query, because both decide the storage design.
2. Write path: the logging SDK or ingest API validates the event and returns 202 quickly, then puts it on a durable queue (Kafka, SQS or Redis Streams). Consumers batch-insert into storage, so a burst of traffic builds queue lag instead of failing client calls.
3. Storage: keep the small envelope fields (tenant_id, timestamp, model, latency, tokens, status) in a columnar or time-partitioned store ordered by (tenant_id, timestamp). Put large prompt and response bodies in a separate table or object storage so dashboard scans never read them.
4. Pre-aggregate per-minute and per-hour rollups by tenant and model. Counts and sums add up across buckets. Percentiles do not, so store mergeable sketches (t-digest, DDSketch) or compute them from raw rows for short windows. Bucket by event time and re-aggregate late events up to a watermark.
5. Read path: the dashboard queries rollups, always filtered by tenant first. Results are cached per tenant with a short TTL and refreshed by polling or pushed over SSE/WebSocket. Raw rows are read only for drill-down, with capped time ranges and keyset pagination.

Follow-up

• How do you show a week's p99 latency when you only kept per-minute rollups?
• Ingest traffic jumps tenfold and consumer lag grows: what does the dashboard show, and what do you scale first?
• A customer wants to filter by an arbitrary metadata tag they attach to each call. How does the design handle high-cardinality dimensions?

How do you ensure high availability and low latency in a distributed backend system?

Medium · Architecture & System Design

Approach

1. Set measurable targets first: an availability SLO (99.9% over 30 days leaves about 43 minutes of downtime) and latency as p95/p99 per endpoint, never as an average. Every later choice is judged against these numbers.
2. Redundancy: run stateless services as several instances across availability zones behind a load balancer with health checks. Give the database replicas and automated failover. With asynchronous replication, a failover can lose the last few writes, so state the recovery point you accept.
3. Contain failures: put a timeout on every network call, retry only idempotent operations with exponential backoff and jitter under a retry budget, add circuit breakers and bulkheads around dependencies, and shed load or serve cached or stale data instead of failing everything.
4. Latency: cache hot reads with a TTL and stampede protection (request coalescing), move slow work such as third-party or LLM calls to queues, pool connections, and deploy near users. Keep fan-out small: if one call in a hundred is slow, a request that fans out to 100 servers hits at least one slow call about 63% of the time (1 - 0.99^100). Hedged requests can help.
5. Prove it in production: RED metrics (rate, errors, duration) and distributed tracing per service, alerts on SLO burn rate, and regular failover drills so the recovery path has actually been exercised.

Follow-up

• The primary database fails over mid-write. What happens to in-flight requests, and how does the client know whether its write landed?
• How do you stop retries from turning a slow dependency into a full outage?
• During a network partition, which operations would you keep available and which would you refuse?

Explain how you would implement rate limiting for a public-facing API.

Medium · Architecture & System Design

Approach

1. Decide what you limit and per whom: the API key or tenant for authenticated calls, falling back to IP for anonymous ones. Set limits per plan and per endpoint, and weight expensive endpoints (e.g. ones that call an LLM) more than cheap reads.
2. Compare the algorithms. A fixed window counter is simple but allows up to twice the limit across a window boundary. A sliding window log is exact but stores one entry per request. A sliding window counter approximates using the previous window's weighted count. A token bucket (capacity b, refill rate r) allows bursts up to b and averages r, which suits most public APIs.
3. In a distributed setup, keep the state in a shared store such as Redis and make the check atomic: a Lua script, or INCR with the expiry set when the key is created. Never do a separate GET then SET, which races. A local in-process pre-check cuts latency in exchange for some accuracy.
4. Contract: reject with HTTP 429 Too Many Requests and a Retry-After header, and expose limit/remaining/reset headers (commonly X-RateLimit-*) so clients can back off before they hit the limit. Document the limits per plan.
5. Placement and failure mode: enforce coarse limits at the gateway or edge and per-tenant business limits in the application. Decide whether to fail open or closed when the limiter store is down. Fail-open protects availability; fail-closed protects downstream cost.

Follow-up

• Redis becomes unreachable for a few minutes. What does your limiter do, and why?
• How would you enforce one global limit for a tenant whose traffic reaches several regions?
• A client retries immediately on every 429. How do you protect the system and steer the client toward correct backoff?

Describe a situation where you had to optimize the performance of a slow application. What steps did you take?

Medium · Past Experience & Execution

Approach

1. Open with the measured symptom and the baseline: which endpoint or page, which percentile, under what load (e.g. 'p95 of the export endpoint was 4 s'), and how you reproduced it. A story without before-and-after numbers is hard to believe.
2. Show how you found the bottleneck before changing anything: tracing or APM span breakdowns, a profiler flame graph (py-spy for Python, --cpu-prof or clinic for Node.js), the database slow query log and EXPLAIN (ANALYZE, BUFFERS) on the worst queries.
3. Name the actual cause and the matching fix. Typical causes: N+1 queries (eager loading or batching), a missing or unusable index causing a sequential scan, CPU work blocking the Node.js event loop (worker threads or a job queue), unbounded result sets (pagination), and sequential external API calls (run them concurrently with Promise.all or asyncio.gather).
4. Change one thing at a time and re-measure at the same percentile under comparable load. Say what you ruled out and why, so it is clear the fix came from reasoning and not guesswork.
5. End with the result and the cost: the new numbers, what you accepted in exchange (cache staleness, extra memory, more complex code), and how you kept it from regressing (a latency alert, a benchmark in CI, or a query-count assertion in tests).

Follow-up

• How did you confirm the improvement held in production and not only in a local benchmark?
• If the fix was a cache, how is it invalidated, and what does a user see when it is stale?
• What was the second-biggest bottleneck, and why did you leave it?

How do you approach writing tests for your code, and how do you decide what needs test coverage?

Medium · Past Experience & Execution

Approach

1. Describe the layers you use: many fast unit tests on pure logic, a smaller set of integration tests against real dependencies (a real PostgreSQL in a container instead of a mocked ORM), and a few end-to-end tests on the user flows that matter most.
2. Choose coverage by risk, not by a percentage. Test heavily where a bug costs money, data or trust: ingestion, billing, auth, migrations and complex branching. Test lightly on thin glue code. Coverage figures are a floor signal, and branch coverage tells you more than line coverage.
3. Turn every production bug into a failing test first, then fix it. Cover the boundaries: empty and maximum inputs, Unicode, time zones and DST, duplicate or retried requests (idempotency) and concurrent updates.
4. For code that calls an LLM API, mock the provider client at the boundary in unit tests and use recorded fixtures for integration tests. Judge output quality with an evaluation set and scored metrics, not exact-string asserts, because responses vary between runs.
5. Keep the suite fast and deterministic in CI: no real clocks or network in unit tests, flaky tests quarantined and fixed quickly, and contract tests on public API shapes so clients do not break silently.

Follow-up

• How would you test a feature whose output comes from an LLM and changes between runs?
• Tell me about code you deliberately left without tests. Why was that the right call?
• A test fails one run in twenty in CI. How do you find the cause?

Why are you interested in joining an early-stage startup in the AI observability space?

Medium · Product Mindset & Startup Fit

Approach

1. Show you understand the problem space candidates describe for this company: software for monitoring, evaluating and securing LLM applications. Explain why it is hard in plain engineering terms: outputs vary between runs, quality is hard to measure automatically, and a change of prompt or model can cause silent regressions in cost, latency or accuracy.
2. Connect it to your own work: an LLM feature you shipped and struggled to debug, a logging or telemetry pipeline you built, or an evaluation you had to invent. One concrete link to your experience is better than a general view on AI.
3. Give your reasons for an early-stage company honestly and specifically: ownership across backend and product, a short path from customer feedback to shipped code, and breadth over specialisation. Acknowledge the trade-offs you accept, such as less structure and shifting priorities.
4. Avoid the answer that sinks this question: 'AI is the future' with no sign of knowing what observability means for LLM apps. End with an informed question about the stack or the hardest scaling or evaluation problem.

Follow-up

• Which methods do you know for evaluating LLM output quality (reference-based metrics, LLM-as-judge, human labelling), and where does each fail?
• If you joined, what would you want to build or improve first, and why?
• What concerns you about working at an early-stage company, and how have you dealt with that before?

How do you prioritize your engineering tasks when multiple features are deemed "critical"?

Medium · Product Mindset & Startup Fit

Approach

1. Make the conflict explicit: list the 'critical' items and score each on impact (users or revenue affected, customer commitments), urgency (a real deadline or active incident), effort, and whether a delay can be undone. Incidents, data loss and security issues come first by default.
2. Rank with a simple, visible method, such as cost of delay divided by effort or an impact/effort grid, so others can challenge the ranking instead of your judgement.
3. Where the trade-off is a business decision, put it to the person who owns it, with options and consequences ('A ships Friday if B slips a week'). Then tell everyone affected what is slipping.
4. Cut scope before adding hours: ship the smallest version that unblocks each customer, and move the rest to later. Back it with a real example that includes the outcome.
5. Avoid answering 'I just work harder and do all of them'. It shows no prioritising and sounds unsustainable.

Follow-up

• Two founders each tell you their feature is the top priority. What do you do?
• Tell me about something you deprioritized that turned out to matter. What did you change afterwards?
• An incident lands halfway through a feature with a customer deadline. How do you split your time?

Describe how you balance technical debt with the need to ship features rapidly.

Medium · Product Mindset & Startup Fit

Approach

1. Separate kinds of debt: deliberate shortcuts taken for speed versus accidental mess, and debt in code that changes weekly versus code nobody touches. The interest is how often it slows changes or causes incidents, and that decides what gets paid down.
2. State where you do not cut corners: data integrity, security and auth, schema changes or migrations that are hard to undo, and public API contracts. Elsewhere, accept shortcuts if they sit behind a clean interface and can be replaced later.
3. Make debt visible: record each shortcut with the condition that triggers paying it back (e.g. 'rewrite when tenants exceed N' or 'when this endpoint gets a third caller'). Pay it down while doing feature work in the same code instead of in a separate cleanup project nobody approves.
4. Use one real example with numbers: the shortcut, what it let you ship, what it later cost, and how and when you repaid it.

Follow-up

• Tell me about a shortcut that came back to hurt you. What would you do differently?
• How do you convince a founder or PM to spend time on debt that has no visible user impact?
• How do you tell when a quick fix has become worth a rewrite?

How do you stay updated with the rapidly changing landscape of software engineering and AI tooling?

Medium · Product Mindset & Startup Fit

Approach

1. Name concrete sources tied to your work: model provider changelogs and API docs (OpenAI, Anthropic), release notes for the frameworks you ship on (Node.js, TypeScript and Python libraries), and source code or issue threads of open-source tools you depend on.
2. Show that you test things hands-on: build a small prototype or benchmark against a real problem before adopting anything, and measure it (latency, cost, quality on a small evaluation set) instead of trusting announcements.
3. Give one example of a tool or technique you adopted and why, and one you tried and dropped, with the reason. Rejections show judgement better than a reading list does.
4. Explain how you bring a new tool into a team: a short write-up, a contained trial behind an interface, and a way to roll back. Avoid answering with a list of newsletters and nothing you applied.

Follow-up

• What is a tool or technique you tried recently and decided not to use? Why?
• A new model is released. How would you decide whether to switch a production feature to it?
• How do you tell a lasting shift in tooling from hype?

Edge instances grow 400 MB per hour until the nightly restart

Hard · memory leak · heap analysis · metric cardinality

Edge API instances start at 700 MB resident and grow about 400 MB/hour; a nightly rolling restart has hidden it for weeks. Growth continues unchanged when request rate halves overnight, p99 degrades in the last hours before an instance is recycled, and heap used immediately after a forced full GC rises monotonically. The service holds no product state. Name the discriminating measurement that separates the plausible causes, give the most likely cause, and give the fix and how you would verify it.

Approach

1. Separate resident memory from live heap first, because they fail differently. Resident size can grow from fragmentation, native buffers or thread stacks while the heap is flat; heap used after a full GC rising monotonically is the measurement that says objects are reachable and not being released. You already have it, so this is retention, not fragmentation, and that closes off half the candidate list.
2. Use the rate's independence from traffic as the discriminator. Growth that continues at half the request rate rules out per-request objects that are merely slow to collect and points at a structure that grows with distinct values observed rather than with call volume. Write the candidates that have that property: a metrics registry keyed on a high-cardinality label, an unevicted cache, an interner, a per-key lock map.
3. Take two heap snapshots an hour apart and diff by retained size, reading the dominator tree, not by allocation count or instance count. Expect one root holding a map with millions of entries, then follow the reference chain to the code that inserts and never removes. Allocation profilers point at churn, which is the wrong signal here.
4. The candidate that fits this service is an observability label carrying an identifier, such as a request path recorded before templating so that /v1/resources/48213 becomes its own metric series. That grows with distinct ids seen, is independent of rate, and explains the late p99 degradation, since GC cost rises with the size of the live set.

Follow-up

• Post-GC heap is now flat but resident size still creeps. What are you looking at, and does it matter?
• How would you have detected this before an OOM, given the nightly restart masked the trend?

Suggested practice checklist

The plan follows the reported question types: a loop map and story bank first, then the system design questions, the past-experience questions, and startup fit.

Day 1 — Map the loop and build a story bank

• Write out the four reported rounds and what each is reported to be: Exploratory Screen, Technical Discussion, Leadership Discussion, Final Evaluation.
• List four or five projects from your resume, each with your role, the stack, one trade-off, one failure or surprise, and one measurable result.
• Mark which project best answers each past-experience question: end-to-end feature, slow application, production mistake, testing, disagreement.

Deliverable: A one-page loop map plus a story-bank table of four or five projects mapped to the past-experience questions.

Day 2 — Real-time analytics dashboard and schema design

• Answer the question about a dashboard showing real-time analytics from millions of LLM logs aloud, sketching ingestion, queue, store, rollups, API and UI.
• Write a PostgreSQL schema for a user-facing analytics application: tables, keys, indexes and time partitioning.
• List the three queries the dashboard must serve and the index or rollup that serves each.

Deliverable: A component diagram and a schema with three target queries, each paired with its index or rollup and the staleness you accept.

Day 3 — Storage trade-offs and availability

• Write a comparison for storing unstructured AI telemetry: JSONB in PostgreSQL, a document store, and a columnar store, judged on query flexibility, write volume, consistency and operations.
• Build a failure table for a distributed backend: dependency down, replica lag, hot key, slow query. For each, write the mitigation (timeouts, caching, replicas, degraded mode) and the latency cost.
• Answer the availability and low-latency question aloud, naming the read path and write path separately.

Deliverable: A storage comparison table and a failure table with a mitigation for each scenario.

Day 4 — Rate limiting and provider retries

• Implement a token-bucket limiter in Python or TypeScript with per-key state, and write tests for burst, refill and concurrent requests.
• Write how multiple API instances share limiter state, and what the client receives on rejection.
• Outline a retry policy for calls to an external LLM provider: backoff with jitter, retry limits, idempotency, and what you log.

Deliverable: Working limiter code with passing tests, a one-page retry policy, and the two practice exercises completed.

Day 5 — Performance, testing and debugging stories

• Write the slow-application story as baseline, hypothesis, change and re-measurement, with the numbers you can defend.
• Write your testing approach: what you cover at unit and integration level, what you skip, and how you decide.

Deliverable: A written performance-story outline, a one-page testing approach, and your spoken debugging narrative for the memory-growth exercise.

Day 6 — Behavioral stories aloud

• Record yourself answering the end-to-end ownership, production-mistake, disagreement and little-direction prompts using Situation, Task, Action, Result.
• Replay the recordings and mark where you say 'we' without naming your own decision, and where a result lacks a number or an observable outcome.
• Re-record the weakest two answers with those gaps fixed.

Deliverable: Four STAR outlines, each with your decision, a measurable result and a one-line lesson.

Day 7 — Startup fit and full rehearsal

• Write answers for why an early-stage AI observability company, how you prioritize when several features are called critical, how you balance technical debt against shipping, and how you keep up with AI tooling.
• Prepare questions for the CTO and founders about the stack, scaling challenges and product direction.
• Run a mock conversation with a friend who opens with broad questions, and practice steering each answer into a concrete example from your story bank.

Deliverable: Written answers to the four startup-fit questions and a list of questions to ask, plus a mock conversation completed.

Stories to bring to the Athina Ai loop

Candidates report that the leadership and final rounds are conversational and that your past work is the main evidence in them. The prompts below all ask for a story from your own career, so prepare four or five that can flex across them. Each story should show a decision you made, the alternative you rejected, and a result you can state plainly. Candidates also pass on the tip to structure answers as Situation, Task, Action, Result and to move into technical detail without waiting to be asked.

1. Tell me about a time you had to deliver a project with highly ambiguous requirements.
2. Describe a situation where you had to push back on a product requirement because of technical constraints.

Tell me about a time you owned a feature from end to end. What were the biggest technical hurdles?

Medium · Past Experience & Execution

Approach

1. Choose a feature where you owned the outcome from requirement to production and after, not one where you implemented a ticket. Open with two sentences of situation: who needed it, what problem it solved, and where your ownership started and stopped.
2. Name two or three concrete technical hurdles and, for each, the options you weighed and why you chose one: a schema change on a live table, a slow query, an unreliable third-party API, an unclear requirement.
3. Cover the whole lifecycle: how you scoped it, how you tested it, how you rolled it out (feature flag, staged release), what you monitored, and what you fixed after launch. Ownership shows in the part after the merge.
4. Close with a result you can defend (latency, adoption, error rate, support tickets) and one thing you would change. The mistake that sinks this story is narrating the team's work in the plural without saying which decisions were yours.

Follow-up

• Which decision in that project would you reverse today?
• How did you know the feature was working once it was live?
• What did you cut from scope, and who agreed to the cut?

Walk me through a time you made a significant mistake in production. How did you handle it and what did you le

Medium · Past Experience & Execution

Walk me through a time you made a significant mistake in production. How did you handle it and what did you learn?

Approach

1. Pick a real mistake you caused or contributed to, with real impact: a bad migration, a misconfigured limit, a missing guard on a retry. A disguised success or a trivial slip reads as avoiding the question.
2. Say in your first sentence that you made the mistake, then follow the incident in order: how it was detected (alert or user report), how you contained it (rollback, flag off, who you told), the root cause, the fix, and the prevention.
3. Give the blast radius honestly: which users or data were affected and for how long, if you know. Separate the technical cause from the process gap that let it reach production.
4. Make the lesson a concrete change: a test, an alert, a canary step, a review checklist, a runbook. A lesson like 'I am more careful now' is the failure mode, as is blaming a teammate or the tooling.

Follow-up

• How did you communicate with affected people while the incident was still open?
• What check would have caught this before release?
• Did your fix remove this class of problem or only this instance?

Tell me about a time you disagreed with a technical decision made by a peer or manager. How was it resolved?

Medium · Past Experience & Execution

Approach

1. Choose a disagreement about technical substance, such as a framework choice, a schema design, build versus buy, or a rollout plan, where both positions were defensible. Avoid a story where the other person was simply wrong.
2. State the other person's position in its strongest form first, then your own and the criteria you were using (risk, cost, time to ship, operational burden). This shows you understood the disagreement and did not only argue it.
3. Describe how evidence settled it: a small prototype, a benchmark or load test, a written comparison of trade-offs, or escalation to whoever owned the decision. Give the outcome even if you were overruled, and say how you committed to the decision afterwards.
4. Finish with the working relationship and what you took from it. Candidates report that this role involves talking directly with founders and leadership, so a story in which you disagreed with a more senior person constructively is a good fit.

Follow-up

• What would have changed your mind?
• What would you have done if the manager still said no?
• Did the decision turn out to be right, and how do you know?

Tell me about a time you had to build something quickly with very little direction or documentation.

Medium · Product Mindset & Startup Fit

Approach

1. Choose a case with unclear requirements and little or no documentation: a prototype for a customer, an internal tool, or an integration with an under-documented API. Say plainly what was missing at the start.
2. Show how you created direction yourself: you asked the requester two or three questions to pin down what success looked like, wrote your assumptions down, and defined the smallest slice that could ship and be tested by a real user.
3. Name the trade-offs. State what you deliberately skipped (edge-case tests, scaling work, polish) and what you refused to skip (data correctness, authentication, logging), and that you recorded the shortcuts as follow-up work.
4. Leave documentation behind: you were the person who got none, so say what you wrote for the next person. Close with what shipped, what first-user feedback changed, and how long the shortcuts stayed in place.

Follow-up

• What did you leave out, and did it come back to cause problems?
• How did you check you were building the right thing?
• How did you hand the work over to someone else?