Antler Software Engineer Interview Guide

The 60-second brief

Candidates describe the Software Engineer role at Antler as building software for startups and entrepreneurs. The work they list is designing, coding and testing new features, fixing and debugging existing applications, taking part in code reviews, and working beside product managers and UX/UI designers. Candidate reports do not name specific products, services or internal tools, so spend time before the first call learning what Antler ships to founders and be ready to say which part of it you would want to work on.

This guide is for software engineers interviewing at Antler. The requirements candidates describe ask for about 2-5 years of experience, one major language (Python, Java or JavaScript are the examples given), web frameworks and RESTful API work, and databases with data modeling. Cloud platforms such as AWS or Azure, CI/CD and DevOps practice, design patterns and agile tooling are listed as extras. The guide covers the two reported rounds, the reported coding, system design, debugging and behavioral questions, and a seven-day schedule that practises them.

Candidates report two rounds over roughly 2-4 weeks: an Initial Screening Call, then Technical Interviews that may include coding challenges or take-home assignments.

Focus areas

• Take-home Technical Assignments
• Engineering Manager Technical/Design Interviews
• Technical Task Execution

Overview

The reported question set is broad rather than deep in any one area. It mixes classic coding questions (reverse a linked list, binary search, find duplicates in an array, explain the complexity, DFS versus BFS), design questions (a URL shortening service, a messaging system, high availability, microservices), practical engineering questions (REST versus GraphQL, query optimization, unit testing, SQL versus NoSQL) and debugging or performance case studies. A strong plan therefore gives each family a fixed block of practice instead of going deep on one.

The role as candidates describe it is hands-on product engineering: features built with product managers and designers, bugs chased in existing applications, and code reviews. That makes your own experience a large part of the loop. Several reported prompts ask you to describe a bug you fixed, a project with a particular stack and your role in it, a technical decision you argued for, or a setback you handled. Prepare those stories with real details such as the system, the symptom, the root cause and what changed afterward.

Candidates also report that the technical stage may include a coding challenge or a take-home assignment, and that the exact shape can vary by team or location. Ask the recruiter during the screening call which format applies, which language you may use and whether the technical interview is live. If a take-home is part of your loop, treat it like a small piece of production work: a clear README, tests for the main paths and edge cases, and a short note on what you would do with more time.

The Antler interview process

Candidates describe two stages. Coding format, take-home use and the people you meet can differ by team or location, so confirm the details with the recruiter after the first call.

Initial Screening Call

Candidates report that the process starts with a call about your background and how well you fit the role. The reported material gives no technical questions for this stage, so treat it as the place to present your experience clearly and to learn how the technical stage will run. Have one concise walk through your recent roles, a project you can discuss in depth (the reported question about a project with a specific technology stack is a good rehearsal for it), and a specific reason for wanting to work on software for startups and entrepreneurs.

What to demonstrate

• Whether your background matches the requirements candidates describe: a major language, web frameworks, REST APIs, databases
• How clearly you explain your role in past projects
• Your motivation for the role and for startup-focused work

How to prepare

• Write a short career walk-through that ends at this role, and attach one project where you can name the stack, your own contribution and one decision you made
• Write a two-sentence answer to "What motivates you to work in a startup environment?" backed by an example of owning scope or handling ambiguity
• Prepare questions for the call: which language the technical stage allows, whether there is a take-home assignment, and who conducts the technical interviews
• Look up what Antler builds for founders so your interest sounds informed rather than generic

Technical Interviews

Candidates report technical interviews that may include coding challenges or take-home assignments. Topic labels attached to this stage include Take-home Technical Assignments, Technical Task Execution, System Design and Engineering Manager Technical/Design Interviews. The reported questions cover several families: coding and algorithms, system design, SQL and database questions, and debugging or performance case studies. Practise each family aloud in your chosen language, and for each answer state your assumptions, the trade-off you are making and how you would verify the result.

What to demonstrate

• Working, tested code on a coding challenge or take-home, including how you explain its complexity
• Design discussion: components, data stores and the trade-offs behind them
• How you break down a debugging or performance problem and explain your steps
• How you carry out a concrete technical task from requirements to result

How to prepare

• Write reverse-linked-list, binary search and duplicate detection from memory in Python, Java or JavaScript, and state the time and space cost of each without prompting
• Outline a URL shortening service and a messaging system on paper: APIs, storage, key or ID generation, delivery guarantees and the first bottleneck
• Rehearse a debugging sequence for an intermittent failure: what changed, what the logs and metrics show, how to reproduce it, how to confirm the fix
• If your loop includes a take-home, practise a small API task end to end: schema, endpoints, tests and a README listing assumptions and trade-offs

Where candidates lose points

Reversing a linked list while losing the rest of the chain, or skipping the empty-list and single-node cases

Save the next pointer before you redirect the current node's link, and trace a three-node list on the board before you say you are done. Test the empty list, one node and two nodes, then give the O(n) time and O(1) space cost for the iterative version.

Answering REST versus GraphQL or SQL versus NoSQL with two lists of definitions and no decision

For each pair, state a concrete scenario where each one wins, such as over-fetching and many round trips for GraphQL clients, or HTTP caching and simple resources for REST. For databases, talk about the access patterns, consistency needs and schema changes in an actual product.

Designing the URL shortener or messaging system by naming components with no numbers, no failure story and no first bottleneck

Open by asking about scale and read-to-write ratio, then sketch the APIs and data model. For the shortener, explain how short keys are generated without collisions and where redirects are cached. For messaging, say what delivery guarantee you promise and what happens when a consumer is down.

Telling a bug or setback story that lists symptoms but never states the root cause, how it was confirmed, or what prevented a repeat

Structure each engineering story as symptom, evidence, root cause, fix, prevention. Keep the evidence concrete: a log line, a metric, a failing test or a reproduction recipe.

Treating a take-home or coding challenge as only a puzzle and handing it in without tests, a README or stated assumptions

Ask what the exercise should and should not cover, cover the main path and edge cases in tests, and write a short note on trade-offs you made and what you would add next.

Questions & practice

Write a function to reverse a linked list.

Medium · Coding / Algorithms

Approach

1. Iterative three pointers: prev = None, curr = head; while curr: nxt = curr.next; curr.next = prev; prev = curr; curr = nxt; return prev. O(n) time, O(1) extra space.
2. Save curr.next before overwriting it; losing that reference drops the rest of the list and is the usual bug.
3. Recursive version: reverse the rest, then head.next.next = head and head.next = None. Still O(n) time but O(n) call stack, so long lists can exceed Python's default recursion limit of 1000.
4. Edge cases: empty list, single node, two nodes; after reversing, check that the old head's next is None so no cycle remains.
5. Variants to have ready: reverse a sublist between positions m and n, reverse in groups of k, and a doubly linked list where each node swaps its prev and next.

Follow-up

• Reverse only the nodes between positions m and n in one pass.
• Reverse the list in groups of k nodes.
• Check whether a linked list is a palindrome in O(1) extra space.

How would you implement a binary search algorithm?

Medium · Coding / Algorithms

Approach

1. Precondition: sorted input or a monotonic predicate. Closed interval: lo = 0, hi = n - 1, while lo <= hi, mid = lo + (hi - lo) // 2, move lo = mid + 1 or hi = mid - 1, return -1 when not found. O(log n) time, O(1) space.
2. Compute mid as lo + (hi - lo) / 2 in Java or C++, since (lo + hi) / 2 can overflow a 32-bit int; Python integers do not overflow.
3. Lower bound with a half-open range [lo, hi): while lo < hi, if a[mid] < target then lo = mid + 1 else hi = mid; this returns the first index with a[i] >= target and handles duplicates. Python's bisect_left and bisect_right implement both bounds.
4. Know library behaviour: Java's Arrays.binarySearch returns -(insertion point) - 1 when the key is absent and does not say which duplicate it finds.
5. Edge cases: empty array, one element, target below the minimum or above the maximum, duplicates, and the infinite loop caused by setting lo = mid without the + 1.

Follow-up

• Find the first and last positions of a target in a sorted array with duplicates.
• Search a sorted array that was rotated at an unknown pivot.
• How would you binary search when the array length is unknown?

Solve a problem involving data structures, such as finding duplicates in an array.

Medium · Coding / Algorithms

Approach

1. Clarify the output (boolean, first duplicate, all duplicates or counts), whether the input may be modified, the value range and the memory limit.
2. Brute force compares every pair: O(n^2) time, O(1) space.
3. Sort, then scan adjacent elements: O(n log n) time; extra space stays O(1) only with an in-place sort such as heapsort, and sorting mutates the input.
4. Hash set in one pass, returning when a value is already seen: O(n) average time, O(n) space; use a hash map (collections.Counter in Python) when counts are needed.
5. Constrained variant with n + 1 values in 1..n: Floyd's cycle detection finds the duplicate in O(n) time and O(1) space without modifying the array; if mutation is allowed, mark seen values by negating a[value - 1].

Follow-up

• The array holds n + 1 integers from 1 to n: find the duplicate without modifying it, in O(1) extra space.
• Return whether two equal values sit within k indices of each other.
• How would you find duplicates in a file too large to fit in memory?

Explain the time complexity of your solution.

Medium · Coding / Algorithms

Approach

1. Count how the work grows with input size: find the dominant operation, multiply nested loops (two nested loops over n give O(n^2)), recognise halving as O(log n), and drop constants and lower-order terms.
2. Use the cost of each operation: hash map get and put O(1) average but O(n) worst case, sorting O(n log n), heap push and pop O(log n), dynamic array append amortized O(1), Python list.insert(0, x) and pop(0) O(n).
3. For recursion write the recurrence: merge sort T(n) = 2T(n/2) + O(n) = O(n log n); naive Fibonacci is exponential, memoized O(n); count the call stack in space.
4. Give time and space, and best, average and worst case where they differ: quicksort averages O(n log n) and degrades to O(n^2).
5. Name the variables: O(V + E) for graph traversal, O(n + m) or O(n * m) for two inputs; flag hidden costs such as slicing copies or repeated string concatenation.

Follow-up

• What does building a string by concatenation in a loop cost, and how would you fix it?
• Why is appending to a dynamic array O(1) amortized?
• Your solution is O(n log n): can it be done in O(n), and at what cost in space?

What is the difference between depth-first search (DFS) and breadth-first search (BFS)?

Medium · Coding / Algorithms

Approach

1. BFS explores level by level with a FIFO queue (collections.deque in Python); DFS goes as deep as possible first, using recursion or an explicit stack. With an adjacency list both run in O(V + E) time, with O(V) for the visited set.
2. BFS gives shortest paths by edge count in unweighted graphs; DFS does not. Weighted graphs with non-negative weights need Dijkstra.
3. DFS fits cycle detection, topological sort, connected components, backtracking (permutations, path existence) and strongly connected components (Tarjan, Kosaraju).
4. Memory: the BFS queue can hold an entire level, which is large on wide graphs; DFS memory grows with depth, and deep recursion can overflow the stack, so switch to an iterative stack.
5. Implementation details: in BFS mark nodes visited when enqueued so none is queued twice; trees need no visited set; on grids, check bounds and obstacles for the four neighbours.

Follow-up

• Find the shortest path from the top-left to the bottom-right of a grid with obstacles.
• Detect a cycle in a directed graph, and explain why the undirected check does not work there.
• Produce a course order from a list of prerequisite pairs.

How do you optimize a database query for performance?

Medium · Technical / Domain Questions

Approach

1. Measure before changing anything: find the slow statement in the slow-query log or pg_stat_statements, then read EXPLAIN (ANALYZE, BUFFERS) in PostgreSQL or EXPLAIN in MySQL for sequential scans, bad row estimates, sorts spilling to disk and the join methods chosen.
2. Index for the predicate: a composite index with equality columns first and the range column last; a covering index (INCLUDE in PostgreSQL) allows an index-only scan. Every index costs write time and storage, so I add only what the plan needs.
3. Keep predicates sargable: WHERE LOWER(email) = ..., WHERE DATE(created_at) = ..., implicit type casts or LIKE '%x' stop a plain B-tree index being used; I rewrite as a range or add an expression index.
4. Cut the work: select only needed columns instead of SELECT *, replace OFFSET pagination with keyset pagination (WHERE id > :last_id ORDER BY id LIMIT n), and replace ORM N+1 loops with a join or one batched IN lookup.
5. If the plan is wrong because statistics are stale, run ANALYZE; beyond one query, consider caching, read replicas, materialized views or partitioning a large time-series table.

Follow-up

• You added an index and the planner still chooses a sequential scan: when is that the right choice?
• Does a composite index on (a, b) help a query that filters only on b?
• How would you speed up COUNT(*) on a table with hundreds of millions of rows?

Discuss trade-offs between SQL and NoSQL databases in application design.

Medium · System Design / Architecture

Approach

1. Relational databases (PostgreSQL, MySQL): enforced schema, joins, ACID multi-row transactions and ad-hoc queries; scaling writes past one primary means sharding, which pushes cross-shard joins into the application.
2. NoSQL is several families: key-value (Redis, DynamoDB), document (MongoDB), wide-column (Cassandra), graph (Neo4j). They model data around known access patterns, denormalize, and partition horizontally by key.
3. Trade-offs: flexible queries and joins versus predictable single-key latency at scale; schema enforcement versus flexibility (schema-on-read still needs migrations in code); many NoSQL stores default to eventual or tunable consistency, and their transactions come with limits.
4. Decide from the workload: entities with integrity constraints (users, orders, payments) fit SQL; very high write volume read by a known key (events, sessions, time series) fits NoSQL; many systems use both, such as PostgreSQL as the record plus Redis as a cache.
5. Mention the middle ground: PostgreSQL JSONB for semi-structured fields, and read replicas plus caching before reaching for a second database.

Follow-up

• How would you model users and their orders in a document store?
• What does eventual consistency mean for a user who edits a profile and reloads the page?
• How would you move a growing table from PostgreSQL to a NoSQL store without downtime?

Explain the difference between REST and GraphQL.

Medium · Technical / Domain Questions

Approach

1. REST models resources as URLs acted on with HTTP verbs (GET, POST, PUT, PATCH, DELETE) and status codes; GraphQL exposes one endpoint (usually POST /graphql) with a typed schema, and the client selects exactly the fields it wants through queries, mutations and subscriptions.
2. Fetching: a REST screen may need several round trips (user, then orders) or receive unused fields; GraphQL resolves a nested selection in one request, but naive resolvers cause N+1 database calls, which I fix with batching such as DataLoader.
3. Caching: REST GETs cache by URL in browsers and CDNs via Cache-Control and ETag; GraphQL POSTs bypass HTTP caching, so I use persisted queries sent as GET or a normalized client cache (Apollo, Relay).
4. Errors and evolution: REST signals failure with status codes and versions via URL or header; GraphQL often returns 200 with an errors array next to partial data, and evolves by adding fields and marking old ones @deprecated.
5. Choice: REST for simple CRUD, public APIs, file uploads and edge caching; GraphQL when several clients (web, mobile) need different shapes of the same data, with depth and cost limits on incoming queries.

Follow-up

• How would you stop a client from sending a deeply nested GraphQL query that overloads the database?
• How would you avoid N+1 queries in a resolver that returns users and their posts?
• How would you version a REST API without breaking mobile clients still on an old release?

Describe a challenging bug you encountered and how you resolved it.

Medium · Technical / Domain Questions

Approach

1. Pick a bug with a real diagnostic path (race condition, memory leak, time-zone or encoding error, stale cache) and open with the symptom, who it affected and how it was detected.
2. Walk the sequence in order: how I reproduced it (or why it would not reproduce locally), how I narrowed it with logs, metrics, git bisect or a minimal failing test, and which hypotheses I ruled out and how.
3. Name the root cause at code level, for example two workers doing read-modify-write on the same row without a lock, fixed with an atomic UPDATE or SELECT ... FOR UPDATE.
4. Close with prevention: a regression test that fails on the old code, an alert on the signal that would have caught it earlier, and any change to review or tooling.
5. The answer falls flat if the bug was a typo found in minutes, or if the cause is still unknown and the fix was a restart.

Follow-up

• How did you confirm the fix worked rather than the bug simply going quiet?
• What would have let you find it faster?
• How did you communicate impact to users or teammates while it was open?

What is the purpose of unit testing, and how do you implement it?

Medium · Technical / Domain Questions

Approach

1. Purpose: verify one unit (a function or class) in isolation, quickly and deterministically, so regressions surface on every change and the tests document expected behaviour; integration and end-to-end tests cover what units cannot.
2. Structure each test as Arrange-Act-Assert, one behaviour per test, named for that behaviour, using the stack's framework: pytest for Python, JUnit 5 for Java, Jest or Vitest for JavaScript.
3. Isolate dependencies by injecting them and replacing the network, database, clock and randomness with fakes or mocks (unittest.mock, Mockito, jest.fn); over-mocking ties tests to implementation details and makes refactors painful.
4. Cover edge cases with parametrized tables (pytest.mark.parametrize, JUnit @ParameterizedTest): empty input, boundaries, null or None, invalid input and the exception paths.
5. Run the suite in CI on every pull request; treat coverage as a way to find untested code, not a target, since full line coverage says nothing about the strength of the assertions.

Follow-up

• How would you unit test a function that calls an external payment API?
• When does a mock make a test worse than having no test?
• How would you test code that depends on the current time?

Can you discuss a project where you used a specific technology stack and your role in it?

Medium · Technical / Domain Questions

Approach

1. Choose one project and state the stack layer by layer (for example a React frontend, a Python or Node API, PostgreSQL, a cloud deployment) and the problem it solved, in two sentences.
2. Separate your work from the team's: say in the first person which components I designed or wrote and which decisions I owned.
3. Justify one stack choice with its trade-off and the rejected alternative, such as PostgreSQL over a document store because the data was relational and needed transactions.
4. Give one technical difficulty and how I resolved it, and an outcome number (latency, deploy frequency, users) only if I actually measured it.
5. Be ready to go deep on anything named: schema, API contract, tests, deployment. Listing many technologies without saying what you did with any of them sinks the answer.

Follow-up

• What would you change in that stack if you rebuilt it today?
• What was the hardest bug or bottleneck in that project?
• How did you test and deploy it?

Design a URL shortening service. What components would you include?

Medium · System Design / Architecture

Approach

1. Scope: shorten a long URL, redirect by code, optional custom alias and expiry, click analytics. Redirects far outnumber creations, so the read path dominates. Base62 with 7 characters gives 62^7, about 3.5 trillion codes.
2. API: POST /urls with the long URL returns a code; GET /{code} redirects with 301 (browsers cache it, so fewer clicks reach the server) or 302 (every click hits the server and can be counted).
3. Code generation: a unique ID (database sequence, ID ranges handed to each server, or Snowflake-style IDs) encoded in base62 avoids collisions but is guessable; a truncated hash or random code needs a unique constraint and retry on collision.
4. Storage and reads: map code to long URL, created_at and expiry in a key-value store or a PostgreSQL table keyed by code, sharded by code; put Redis in front for hot codes and return 404 or 410 for missing or expired ones.
5. Keep analytics off the redirect path by publishing click events to a queue such as Kafka and aggregating them asynchronously; rate-limit creation and check submitted URLs against abuse lists.

Follow-up

• How do you generate unique codes across many app servers without a single bottleneck?
• The same long URL is submitted twice: return the same code or a new one?
• How would you count clicks per code without slowing the redirect?

How would you approach designing a messaging system for a large-scale application?

Medium · System Design / Architecture

Approach

1. Clarify which system is meant: user-to-user chat (1:1 and groups, history, read receipts, presence) or an asynchronous broker between services; then fix delivery and ordering guarantees before drawing anything.
2. For chat: clients keep WebSocket connections to stateless gateway servers, a registry in Redis maps each online user to a gateway, and offline users get a push notification.
3. Write path: the server assigns a message ID and a per-conversation sequence number, persists the message (wide-column store partitioned by conversation_id, clustered by sequence) before acking, then fans out through pub/sub or Kafka to recipients' gateways.
4. Semantics: at-least-once delivery with a client-generated idempotency key so retries are deduplicated; ordering guaranteed only within a conversation; on reconnect the client syncs from the last sequence number it saw.
5. Scale: shard gateways and storage by conversation, watch hot partitions from very large groups, and choose fan-out on write for small groups versus fan-out on read for huge ones.

Follow-up

• How do you keep order in a conversation when two people send at the same moment?
• What happens to messages sent while the recipient is offline for a week?
• How does fan-out change for a group with 100,000 members?

How would you ensure high availability in a distributed system?

Medium · System Design / Architecture

Approach

1. Set the target first: an availability SLO (99.9% allows about 8.76 hours of downtime a year, 99.99% about 52.6 minutes) plus RTO and RPO; high availability means removing single points of failure.
2. Redundancy at every layer: stateless app servers across several availability zones behind a load balancer with health checks, and auto-scaling groups that replace failed instances.
3. Data layer: replication with automatic failover; synchronous replication gives zero data loss at a latency cost, asynchronous can lose the last writes. Consensus systems such as Raft tolerate f failures with 2f+1 nodes.
4. In code: timeouts on every remote call, retries with exponential backoff and jitter, idempotent operations so retries are safe, circuit breakers, and graceful degradation such as serving cached data.
5. Operations: monitoring and alerting, rolling, blue-green or canary deploys, multi-region for regional outages, and regular failover drills so the failover path is known to work.

Follow-up

• How do you avoid split-brain when the primary database fails over?
• Retries made an outage worse: why, and how do you prevent it?
• What changes when you go from multi-AZ to multi-region?

Describe a microservices architecture and its benefits.

Medium · System Design / Architecture

Approach

1. Define it: an application split into independently deployable services, each owning one business capability and its own data, talking over HTTP or gRPC or through asynchronous messages; contrast with a monolith.
2. Benefits: deploy and scale each service independently, isolate faults, let teams own services end to end, and pick technology per service.
3. Costs: network latency and partial failure, consistency across services (sagas with compensating actions and the outbox pattern instead of two-phase commit), distributed tracing with correlation IDs, plus service discovery, an API gateway and a pipeline per service.
4. Draw boundaries along business domains; shared databases and long chains of synchronous calls produce a distributed monolith with the costs of both designs.
5. Timing: a small team often moves faster with a modular monolith and extracts services when scale or team boundaries require it, using the strangler fig pattern.

Follow-up

• How would you place an order that must reserve stock and charge a card in two different services?
• How do you trace a slow request across five services?
• When would you merge two services back into one?

Given a dataset, how would you identify trends and make recommendations?

Medium · Problem-solving / Case Studies

Approach

1. Pin down the decision the analysis serves, the metric, period and granularity; then profile the data: schema, row counts, missing values, duplicates, outliers, units and time zones.
2. Aggregate over time (daily, weekly) and plot; smooth with rolling averages and separate trend from weekly or yearly seasonality with week-over-week or year-over-year comparisons or a decomposition.
3. Segment by dimensions (region, platform, signup cohort) to see where a change concentrates, using SQL GROUP BY with window functions or pandas groupby and resample.
4. Test robustness: mix shift between segments (Simpson's paradox), tracking or pipeline changes, small samples; attach confidence intervals and keep correlation separate from causation until an experiment or comparison group supports it.
5. Tie each recommendation to a finding, its estimated impact and confidence, and a next step such as an experiment or a metric to monitor.

Follow-up

• Signups rose 20% last month: how do you tell real growth from a tracking change?
• How would you write the weekly trend query in SQL?
• Overall conversion rose while it fell in every region: how is that possible?

Describe how you would handle a sudden increase in user traffic on an application.

Medium · Problem-solving / Case Studies

Approach

1. Triage first: confirm from metrics whether the traffic is real users or bots, and find the saturated resource (CPU, database connections, queue depth, p99 latency).
2. Short term: scale the stateless tier horizontally, rate-limit and shed load with 429 and Retry-After, serve static and cacheable responses from a CDN, and switch off non-essential features with flags.
3. Protect the database: connection pooling (PgBouncer), read replicas for reads, Redis for hot queries, and stampede protection through request coalescing and jittered TTLs.
4. Move slow work (emails, image processing, reports) onto queues with workers so bursts are absorbed instead of blocking requests.
5. Afterwards: load test to know real capacity, tune auto-scaling with headroom, pre-scale for known events, and write up the incident.

Follow-up

• Auto-scaling added app servers and the database fell over: why?
• How would you tell a viral spike from a bot attack?
• What would you switch off first to keep the core feature up?

If tasked with improving software performance, what metrics would you analyze?

Medium · Problem-solving / Case Studies

Approach

1. Start from the user-facing view: latency percentiles (p50, p95, p99, not the average), throughput, error rate and saturation, organized as RED (rate, errors, duration) for services and USE (utilization, saturation, errors) for resources.
2. Resource metrics: CPU, memory and GC pause time, disk I/O, network, thread and connection pool usage, queue lengths.
3. Data and dependencies: slow-query counts and time, cache hit ratio, lock waits, ORM N+1 patterns, and external call latency from distributed traces.
4. For a web frontend: Core Web Vitals (LCP, INP, CLS), time to first byte and bundle size.
5. Method: baseline, profile the hot path (cProfile or py-spy for Python, async-profiler or JFR for Java, Chrome DevTools for JavaScript), change one thing, re-measure under the same load; optimize where the time goes (Amdahl's law).

Follow-up

• Why is average latency misleading, and which percentile would you set a target on?
• CPU sits at 30% but latency is high: where do you look?
• How would you prove an optimization helped in production?

Explain your thought process when tackling a new technical challenge.

Medium · Problem-solving / Case Studies

Approach

1. Clarify the problem and the success criteria: inputs, constraints, scale, deadline and what done looks like, restated in my own words.
2. Decompose into subproblems, list the unknowns and the riskiest assumption, and check prior art: documentation, existing code, how others solved it.
3. Prototype the riskiest part first in a timeboxed spike, then compare two options on complexity, performance, maintainability and cost.
4. Build incrementally with tests, get early feedback through code review or a short design write-up, and measure the result against the success criteria.
5. Anchor it in one real example told with "I"; an abstract framework with no example is the usual weak answer.

Follow-up

• Tell me about a time your first approach was wrong: how did you notice?
• How do you decide when to stop researching and start building?
• When do you ask for help instead of continuing to dig?

How would you approach debugging a system that is intermittently failing?

Medium · Problem-solving / Case Studies

Approach

1. Characterize the failure before theorizing: frequency, error type, which hosts, regions, endpoints or users, and timing correlations (traffic peaks, cron jobs, deploys, GC pauses). Intermittent usually means a condition, not randomness.
2. Collect evidence: correlate logs by request or trace ID, check error rate, p99 latency, CPU, memory and connection-pool metrics, and compare failing requests with succeeding ones to isolate the difference.
3. Check the usual suspects: race conditions, exhausted connection pools or file descriptors, memory growth ending in OOM kills, a flaky downstream dependency, one bad host behind the load balancer, mismatched timeouts and retries, clock skew, cache expiry stampedes.
4. Make it reproducible: load test, replay traffic, inject latency or faults, or loop a flaky test until it fails often; then confirm one hypothesis by changing one variable at a time.
5. Fix the root cause, add a regression test and an alert on the signal, and keep the logging that would have shortened the search.

Follow-up

• The failures stop when you add logging: what does that suggest?
• Only one of ten hosts shows the errors: what do you check?
• How would you make a flaky test fail on demand?

Suggested practice checklist

The plan follows the two reported rounds. It starts with the screening call and your project stories, then covers coding, system design, the database and API questions, debugging cases, behavioral prompts and a full mock loop.

Day 1 — Screening call and your project stories

• Write a short career walk-through and pick the one project where you can name the stack, your role and a decision you made; this is the material for the question about a project and your role in it
• Write your answer to what motivates you to work in a startup environment, with one example of owning scope or handling ambiguity
• List three questions for the recruiter: coding language, take-home or live technical stage, and who runs the technical interviews

Deliverable: A one-page script containing the career walk-through, the project summary, the startup-motivation answer and the three recruiter questions.

Day 2 — Coding and algorithms in one language

• Write reverse-a-linked-list iteratively and recursively and compare the stack cost; test the empty, one-node and two-node cases
• Implement binary search with the loop invariant written in a comment, then handle the not-found case and duplicates
• Solve find-duplicates in an array three ways (nested loops, hash set, sort first) and say the time and space cost of each
• Explain DFS and BFS using a small graph: stack or recursion versus queue, and which one finds the shortest path in an unweighted graph

Deliverable: Four working solutions in Python, Java or JavaScript with a short complexity statement written beside each.

Day 3 — System design: shortener, messaging and availability

• Outline a URL shortening service: API, data model, key generation, redirect path with a cache, and what you do when a key collides
• Outline a messaging system: producers, a queue or log, consumers, delivery guarantee, ordering and what happens when a consumer is down
• Write a high-availability checklist for a distributed system: redundancy, health checks, failover and graceful degradation, then say how a microservices split helps and where it adds failure points

Deliverable: Two annotated design sketches (the shortener and the messaging system) plus a one-page high-availability and microservices trade-off sheet.

Day 4 — APIs, databases and testing

• Write a comparison of REST and GraphQL with one scenario where each fits better, covering over-fetching, caching and versioning
• Take one slow query, read its execution plan, and write down the index or rewrite that fixes it; then list when SQL beats NoSQL and when it does not
• Write unit tests for a small function covering a normal case, an edge case and a failure case, and explain what you mocked and why

Deliverable: A REST versus GraphQL comparison note, one before-and-after query with its plan, and one tested function.

Day 5 — Debugging, performance and case studies

• Write a debugging sequence for an intermittent failure: what changed, logs and metrics, a reproduction attempt, a hypothesis you can test, and a fix you can verify
• Plan the response to a sudden traffic spike: measure first, then caching, scaling out, rate limiting and protecting the database
• List the metrics you would collect when asked to improve software performance (latency percentiles, throughput, error rate, resource use) and say which you would look at first
• Practise explaining how you would tackle a technical challenge you have not seen, then write up one real bug from your career as symptom, evidence, root cause, fix and prevention

Deliverable: A debugging runbook, a traffic-spike plan, a metrics shortlist, and one written bug story.

Day 6 — Behavioral stories

• Write stories for the difficult team member, advocating for a technical decision and handling a setback, each with situation, action, result and what you changed
• Write your prioritization method with one concrete example of what you deferred and how you communicated it
• Say all five stories aloud and cut any that run longer than you can hold attention for

Deliverable: Five rehearsed answers, each with a one-line result you can state in a sentence.

Day 7 — Full mock loop and take-home dry run

• Run a mock screening call using your day 1 script and answer the recruiter questions you wrote
• Do a mock technical interview: one coding question, one design question and one debugging case, spoken aloud
• Do a short take-home dry run: build a small REST endpoint with a data store, add tests and write a README with assumptions and trade-offs
• Review every weak answer and redo it

Deliverable: A mock-loop log that records the weakest answer of each type and a small tested API with a README.

Behavioural preparation

The reported behavioral prompts cover conflict, prioritization, advocacy, motivation for startup work and setbacks. The role as candidates describe it involves working with product managers and designers, taking part in code reviews and running several projects at once, so pick stories from those settings. Give each story a decision you made, an observable result and a lesson you applied afterward.

1. Describe a time when you had to influence a team decision.

Tell me about a time you had to work with a difficult team member.

Medium · Behavioral / Leadership

Approach

1. Choose a conflict with a work cause you can describe plainly, such as repeated missed review feedback, a clash over how to structure a shared module, or handoffs that kept slipping. Avoid a story built on personality alone, because it gives the listener nothing concrete to evaluate.
2. Open with two sentences of context: the project, the person's role and what was suffering. Then spend most of the answer on your actions: a private conversation to hear their constraints, a written working agreement (review turnaround, pull request size, who decides on the interface), and escalation to a manager only after those steps failed.
3. Show a result you can observe, for example review cycles that shortened, a feature that shipped, or a recurring argument that stopped. Say what you changed in your own behavior too.
4. The mistake that sinks this answer is casting the other person as a villain or ending with no change. Talk about the person with respect and be honest about your own part in the friction.

Follow-up

• What would you have done if the direct conversation had changed nothing?
• When did you involve your manager, and what did you ask them for?
• What did this teach you about your own contribution to the friction?

How do you prioritize tasks when working on multiple projects?

Medium · Behavioral / Leadership

Approach

1. Describe a method you actually use, not a slogan. A workable one: list every commitment with its deadline, who is blocked by it and the cost of delay; rank by impact against effort; then confirm the ranking with whoever owns each request.
2. Give one real case with two or more projects competing. Say what you did first, what you postponed or reduced in scope, and how you told the affected people before the deadline rather than after it.
3. Cover interruptions. Say how you handle a production bug that arrives mid-task: stabilize first, record the work you paused and re-plan openly. Role descriptions mention working with product managers and designers on several projects at once, so mention how you ask them to resolve conflicts between their requests.
4. Avoid answers such as "I just work harder" or a matrix you have never used. State the cost of your choice, because every prioritization drops something.

Follow-up

• Two product managers each say their request is urgent. What do you do?
• A production incident lands in the middle of a sprint. How do you re-plan?
• How do you tell someone their request has been deferred?

Describe a situation where you had to advocate for a technical decision.

Medium · Behavioral / Leadership

Approach

1. Pick a decision with real alternatives and real cost, such as adding a queue, choosing a data store, doing a refactor before a feature, or a testing strategy. A decision nobody could have disagreed with makes for a weak story.
2. Lay out the options, the criteria you used (delivery risk, operational burden, performance, cost to change later) and the evidence you gathered: a prototype, a benchmark, an incident history or an estimate. Say how you adapted the message to the audience, for instance delivery risk and user impact for a product manager, operational detail for engineers.
3. State what you conceded and the outcome, including any part you got wrong. Showing that you changed course on new evidence is stronger than a story where you simply won.
4. The mistake is a story about authority ("I was the senior person so it went ahead") or one that skips the opposing view. Present the other side fairly.

Follow-up

• What would you have done if you had been overruled?
• Was there a point where you were persuaded that you were wrong?
• How did you check afterward that the decision was the right one?

What motivates you to work in a startup environment?

Medium · Behavioral / Leadership

Approach

1. This is a motivation question, so use evidence from your history instead of a story template. Name two or three things you want from a startup setting, such as owning a feature end to end, working with ambiguity or seeing users react quickly, and attach a concrete moment from your past work to each one.
2. Connect it to this employer without inventing facts: the role is described as building software for startups and entrepreneurs, and the requirements mention an entrepreneurial mindset and a fast-paced background as a plus. Say what draws you to building tools for founders, based on what you have learned about the product.
3. Show that you know the trade-offs: fewer established processes, shifting priorities and a wider scope per person. Give an example of when you handled that kind of change well.
4. The mistake is a generic "I like fast pace" with nothing behind it, or an answer that is mostly about what you will get rather than what you will build.

Follow-up

• What would you miss from working in a larger company?
• Tell me about a time ambiguity or changing priorities caused you real trouble.
• What would you want to learn about the product in your first weeks?

How do you handle failure or setbacks in a project?

Medium · Behavioral / Leadership

Approach

1. Choose a real setback that you contributed to, such as a missed deadline, a bad release or a design that did not scale. A failure that is secretly a strength ("I care too much") will not hold up.
2. Walk through the sequence: how you noticed it, how you limited the damage and told the people affected, how you fixed it, and what the root cause was. Then say what changed in the process afterward, such as a regression test, an alert, a review checklist or a smaller rollout, and whether that change worked.
3. Own your part in one sentence without excuses, and keep the emotional part short. The substance should be what you did next and what you do differently now.
4. The mistake is blaming others or the circumstances, or giving a story where the lesson is vague. Where possible attach a number or an observable result to the recovery.

Follow-up

• How soon did you tell your manager or the affected team, and why then?
• What exactly changed afterward, and did it stick?
• How do you decide between pushing on and cutting scope when a project is going badly?