Archer Software Engineer Interview Guide

The 60-second brief

Archer builds all-electric vertical takeoff and landing aircraft, known as eVTOLs, and positions them within advanced air mobility. Candidates describe the Software Engineer role as work on the systems, infrastructure and software that sit behind the aircraft's flight systems, AI-driven insights and production services. The role is reported to involve working with AI researchers, systems engineers and product teams, joining design reviews, reviewing code and responding to production issues with root-cause analysis.

This guide is for software engineers preparing for Archer's backend, cloud and distributed-systems style loop. The stack candidates describe is Python or Rust, one of AWS, GCP or Azure, microservices, and ideally Kubernetes and Terraform or OpenTofu. It covers the three reported rounds, every reported question (including the battery and power-electronics questions that surprise software candidates), behavioral stories, and a seven-day plan.

Candidates report three rounds over roughly three to five weeks: a recruiter screening, technical deep-dives with engineers and managers, and a panel interview that can take the form of a case study presentation.

Focus areas

• Case Study Presentation
• Power Electronics
• DC/DC Converter Design

Overview

The reported questions fall into four groups. Systems questions ask for a real-time backend for aircraft sensor data, high availability in safety-critical software, Kubernetes deployment of microservices, infrastructure-as-code and a whiteboard debugging process. Trade-off questions compare Postgres with in-memory stores under high throughput and ask how to cut latency in an AI-driven data pipeline. Domain questions touch battery chemistry and power electronics or DC/DC conversion. Behavioral questions cover explaining a concept to a non-technical person, mentoring, pressure and disagreement. Prepare all four groups, because the loop is not a pure algorithms screen.

Candidates also report a case study presentation, where the prompt often arrives a few days ahead and you present a solution and defend its trade-offs. Several reported questions are open-ended, such as re-engineering a component under a given constraint, and candidates are advised to ask clarifying questions to fix the scope before designing. Practise opening every design answer with the data shape, the rates, the failure tolerance and who consumes the output, then build the design from those answers.

Because the stack is Python or Rust, expect to explain depth in the language you choose. For Rust that means ownership and borrowing, how shared state is protected across threads, and channel or async patterns. For Python it means the effect of the GIL, when asyncio helps, and when you need processes. Candidates are also told to expect detailed questions about every project on their resume, so prepare each project as a decision, a constraint and a failure, not as a list of technologies.

The Archer interview process

Candidates describe three stages that move from a recruiter conversation to engineer and manager interviews and then a panel or case study presentation. The descriptions below are what candidates report, not a published process.

Recruiter Screening

A recruiter screening to judge fit for the role. Candidates report that early screens are often remote. Treat it as the place to show that your background matches the stack the role lists (Python or Rust, a major cloud, distributed systems) and that you can say why you want to build software for an eVTOL aircraft maker. Have a short, structured account of your career ready, with one or two projects you can expand on if asked.

What to demonstrate

• Fit between your background and the role's listed requirements
• Clarity when summarising your experience
• Interest in the aircraft and advanced air mobility context

How to prepare

• Write a short career walkthrough that names your language (Python or Rust), your cloud platform and one distributed-systems project, each with its outcome.
• Read up on advanced air mobility and eVTOL aircraft well enough to give a specific reason for applying beyond the word aviation.
• List logistics you may be asked about: location, notice period, and whether you have aerospace, robotics or high-availability experience, which the role lists as a plus.

Technical Deep-Dives

In-depth technical interviews with engineers and managers. Candidates report evaluation areas of technical depth (memory management and concurrency, especially in Rust, scalability patterns and production debugging) and system design (architecture, API design and data flow, failure handling and recovery). This is the stage where question types like the sensor-ingestion design, the high-availability question, the Postgres versus in-memory trade-off, Kubernetes orchestration and the whiteboard debugging process get practised. Talk through your reasoning as you go and state constraints early.

What to demonstrate

• Depth in your chosen language, including concurrency and memory behavior
• Ability to design a scalable, failure-tolerant backend and defend its trade-offs
• A structured method for debugging a production failure

How to prepare

• Rehearse the sensor-data backend design end to end: ingestion, buffering, partitioning, storage, consumers, and what happens when each part fails.
• Prepare a one-page comparison of Postgres and an in-memory store covering durability, query shape, write throughput and recovery after a restart.
• Practise the debugging sequence on a whiteboard: what changed, timeline, metrics, logs, traces, bisect, rollback, then root cause.
• Review Kubernetes rollout mechanics (probes, resource limits, rolling updates) and how Terraform or OpenTofu state is shared and locked.

Panel Interview

A panel interview or case study presentation. Candidates report that the case study prompt often comes a few days in advance and that you present a solution, explain your architectural choices and trade-offs, and show how the design meets the constraints in the prompt. You may meet several team members, including senior engineers and directors. Prepare a written design with the alternatives you rejected, and expect follow-ups on the hardest decision you made.

What to demonstrate

• Clarity of architectural choices and the trade-offs behind them
• How well the solution addresses the constraints in the prompt
• Communication with a mixed audience of engineers and leaders

How to prepare

• Build a short deck or document with the problem, constraints, chosen design, two rejected alternatives and the failure modes you handled.
• Prepare a spoken answer to which design decision was hardest and what you would change now.
• Rehearse the stakeholder, mentoring, pressure and disagreement stories so each ends with a concrete result.
• Practise a re-engineering exercise: take a slow component, state the constraint, find the bottleneck by measurement, change it and show how you would verify the gain.

Where candidates lose points

Starting the aircraft sensor-data design by drawing boxes before establishing the data

Open by asking how many sensors, what message rate and size, how much loss or reordering is tolerable, what latency consumers need and what reads the data. Then derive partitioning, buffering and storage from those answers.

Answering the high-availability question with slogans such as add redundancy and use Kubernetes

List concrete failure modes (process crash, overloaded dependency, network partition, bad input, bad deploy), and for each give detection, graceful degradation, recovery and how you would test it.

Bluffing on battery chemistry or power electronics instead of reasoning from first principles

State your actual level, then reason aloud from basics, for example how voltage sags under load or why switching frequency trades size against heat, and tie it to what the software would monitor.

Presenting a case study with a design but no rejected alternatives

For every major choice, write down what you did not choose and why, plus how the design behaves when the key constraint changes, so follow-ups have prepared answers.

Describing resume projects by technology list, then stalling when asked why a decision was made or what failed

For each project, prepare the constraint, the alternatives, the decision, one failure and its root cause, and one thing you would change. Candidates report detailed questions on every listed project.

Questions & practice

Walk me through the case study presentation you prepared; what was the most difficult design decision you face

Medium · Case Studies & Problem Solving

Walk me through the case study presentation you prepared; what was the most difficult design decision you faced?

Approach

1. Open with the prompt restated in one sentence and the constraints it set (load, latency, cost, safety, team, timeline), along with any assumptions you made where the prompt was silent. Candidates report that the case study prompt can arrive days ahead, so state each assumption explicitly.
2. Show the architecture once at a high level, then go into the one decision you found hardest: the two or three options, the constraint that decided between them, and what you gave up by choosing it.
3. Back the decision with evidence rather than preference: a back-of-envelope estimate, a small prototype or benchmark, or a failure-mode comparison. Say what result would have changed your choice.
4. Cover the failure states and recovery: what happens when each major component is slow or down, and how the design detects that and degrades.
5. End with the weakest assumption, how you would test it first, and what you would build next with more time.

Follow-up

• If the constraint you optimised for doubled, which part of the design breaks first?
• Which assumption are you least sure of, and how would you check it cheaply?
• What would make you reverse the hardest decision you described?

How would you optimize a data pipeline for minimal latency in an AI-driven application?

Medium · Case Studies & Problem Solving

Approach

1. Measure before changing anything: instrument p50 and p99 latency for each stage (ingest, feature computation, queueing, inference, post-processing, response) and find where the end-to-end budget actually goes.
2. Remove structural delay: replace periodic batch jobs with streaming where freshness matters, drop extra disk or network hops, and precompute expensive features into an online feature store or cache so a request only does a lookup.
3. Cut the cost per hop with compact serialization (Protobuf or Arrow instead of JSON), async I/O, connection reuse, and running services close to each other. On the model side, use quantization, a smaller or distilled model, or an optimized runtime such as ONNX Runtime or TensorRT.
4. Make the batching trade-off explicit. Larger inference batches raise throughput but add queueing delay, so dynamic batching with a maximum wait time caps the latency cost. Bound queues so overload sheds requests instead of growing latency without limit.
5. Watch correctness as well as speed: cached or precomputed features can go stale, and online features computed differently from training features cause training-serving skew. Address tail latency with timeouts and hedged requests to replicas.

Follow-up

• Median latency is fine but p99 is several times higher. Where do you look?
• How do you choose the batch size and maximum wait for dynamic batching?
• How do you keep precomputed features fresh without recomputing them on every request?

Given a specific system constraint, how would you re-engineer this component to improve performance?

Medium · Case Studies & Problem Solving

Approach

1. Make the constraint concrete before changing anything: a latency budget, throughput target, memory ceiling, CPU or power limit. Ask how it is measured, and write down the current baseline under a representative load.
2. Profile instead of guessing (a sampling profiler such as py-spy or perf, flame graphs, allocation profiling). Amdahl's law bounds the gain: if a fraction p of the time is sped up by a factor s, overall speedup is 1 / ((1 - p) + p / s), so fix the largest share first.
3. Work through the options layer by layer: a better algorithm or data structure (for example O(n^2) down to O(n log n), or a hash lookup instead of a scan), fewer allocations and copies, batched I/O, caching, then concurrency. In Python, CPU-bound work is limited by the GIL, so use multiprocessing, vectorised libraries or a Rust extension (for example through PyO3) for the hot loop.
4. Protect behaviour while re-engineering: golden-output or property-based tests comparing old and new versions, a benchmark in CI to catch regressions, and a staged rollout behind a flag.
5. State the trade-off you accept, whether that is more memory for less CPU, eventual instead of immediate consistency, or more code complexity, and confirm it still meets every other constraint.

Follow-up

• The profile shows time spread evenly with no hotspot. What now?
• How do you prove the re-engineered component returns exactly the same results?
• What changes if the binding constraint is memory rather than CPU?

Write the update path that detects a concurrent edit

Medium · optimistic concurrency · transactional outbox · audit history

resource carries version INT NOT NULL DEFAULT 1. resource_revision holds revision_id, resource_id, version, actor_user_id, change_kind, patch JSONB, request_id, created_at with UNIQUE (resource_id, version). outbox_event holds aggregate_type, aggregate_id, aggregate_version, event_type, payload, status. A PUT carries the version the client read. Write the exact statements for the single transaction that applies the edit, records the revision and enqueues 'resource.updated', and give the handler's branch on zero affected rows. Then say what PostgreSQL 16 does under READ COMMITTED when two of these updates hit one row at once.

Approach

1. One transaction, three writes, no network call inside it: UPDATE resource SET title = $3, version = version + 1, updated_at = now() WHERE resource_id = $1 AND tenant_id = $4 AND version = $2; then INSERT the resource_revision row at version $2 + 1; then INSERT the outbox_event row at the same aggregate_version. The event goes to a table rather than a broker because no transaction spans both.
2. Branch on the affected-row count before doing anything else. Zero has three causes — stale version, wrong tenant, row gone — so re-read once and map to 409 carrying the current version, or 404 for an id outside the caller's tenant, which also stops the endpoint confirming that another tenant's id exists.
3. State the engine behaviour instead of assuming it. Under READ COMMITTED the second UPDATE blocks on the row lock, and when the first commits PostgreSQL re-evaluates the WHERE clause against the newly committed row, so the version predicate now fails and the statement reports zero rows. Under REPEATABLE READ the identical collision raises SQLSTATE 40001 instead, so the handler must fold both shapes into one conflict response.
4. Keep UNIQUE (resource_id, version) even though the predicate already serialises writers. It is what makes a lost update unwritable if any other path ever reaches the revision table, and it converts a logic bug into 23505 rather than into a silently missing history row.

Follow-up

• A client sends the version it read ten minutes ago and the resource has moved three versions. What is in your 409 so it can resolve the conflict without a full re-fetch?
• Two editors, two disjoint fields, no overlap. Does your answer still refuse the second write, and should it?

Keep soft-deleted accounts from blocking re-registration

Easy · soft delete · unique constraints · engine differences

app_user holds user_id, tenant_id, email CITEXT, password_hash (NULL for SSO principals), email_verified_at, auth_version, status ('invited','active','suspended','deactivated'), created_at, updated_at, deleted_at. Two live accounts for one address inside a tenant must be impossible, but an address freed by a soft delete must be reusable, and the same tenant may delete and re-register it repeatedly. Write the uniqueness DDL for PostgreSQL 16, then the equivalent for MySQL 8 where partial indexes do not exist, and say what each permits once three deleted rows already hold that address.

Approach

1. Start from what is actually unique: not (tenant_id, email), but (tenant_id, email) among live rows. PostgreSQL says that directly — CREATE UNIQUE INDEX app_user_live_email ON app_user (tenant_id, email) WHERE deleted_at IS NULL. A full constraint over the same two columns burns the address permanently the first time someone deletes an account.
2. Keep case-insensitivity in the type or the index, never in the application: CITEXT as given, or UNIQUE (tenant_id, lower(email)) as an expression index where the extension is unavailable. A case-sensitive unique column is exactly how two accounts for one human appear.
3. For MySQL 8 the predicate has to move inside the key: add a discriminator column that is a constant 0 while the row is live and is set to user_id on delete, with UNIQUE (tenant_id, email, deleted_marker). Live rows share the constant and still collide; deleted rows differ from each other and stop colliding.
4. State the NULL variant and its dependency: leaving the marker NULL for deleted rows also works, because a unique index treats NULLs as distinct — true in MySQL, and true in PostgreSQL only under the default NULLS DISTINCT, which PostgreSQL 15 lets you reverse. Check the polarity against the three existing deleted rows: constant-on-live is what preserves the collision you want, and reversing it silently admits duplicate live accounts.

Follow-up

• A deleted account re-registers with the same address the next day. Do the old resource rows follow the new user_id, and how does the API keep the two principals apart?
• How do you honour an erasure request while resource_revision.actor_user_id still references this table?

How would you explain the fundamental principles of battery chemistry and their impact on system design?

Medium · Technical & Domain Knowledge

Approach

1. Start with the mechanism: in a lithium-ion cell, lithium ions move between a graphite anode and a metal-oxide cathode through the electrolyte while electrons flow through the external circuit. Cell voltage depends on the chemistry, typically about 3.0-4.2 V per cell with a nominal value near 3.6-3.7 V for NMC-type cells.
2. Separate energy from power. Capacity sets range, and internal resistance sets how much the voltage sags under load (V = OCV - IR) and how much I^2R heat the cell makes. Cells and chemistries trade energy density against power capability and cycle life.
3. Link the chemistry to pack design: cells in series set the pack voltage and cells in parallel set capacity and current capability. A battery management system watches each cell's voltage, current and temperature, balances cells, and cuts or derates current outside safe limits to prevent thermal runaway.
4. Then cover what this means for software. State of charge cannot be measured directly, so it is estimated by coulomb counting corrected against open-circuit voltage, often with a Kalman filter. State of health tracks capacity fade and rising resistance with cycles and temperature. Cold cells and low charge reduce available power, so limits have to come from the model, not from a fixed number.
5. Name the edge cases: a weak cell that limits the whole series string, sensor faults that look like real over-temperature, and estimator drift after long periods without a rest at open-circuit voltage.

Follow-up

• How would you estimate state of charge while the pack is under heavy, changing load?
• What should the system do when one cell's voltage or temperature diverges from its neighbours?
• How does cold temperature change the power you can safely draw, and how would software enforce that?

How do you ensure high availability and reliability in real-time or safety-critical software systems?

Medium · Technical & Domain Knowledge

Approach

1. Define the terms. Availability is the fraction of time the system serves requests. Reliability is how long it runs without failure. Safety is whether a failure leads to harm. In hard real-time systems, a late answer counts as a failure, so deadlines and worst-case execution time are requirements, not averages.
2. Use redundancy with the arithmetic. Two independent replicas at 99% each give 1 - 0.01^2 = 99.99%, but only if their failures are independent. Components in series multiply their availabilities. Common-mode faults (same code, same power, same bad input) break that independence, so add diversity where it matters.
3. Detect faults and contain them with heartbeats and watchdog timers, plausibility and range checks on inputs, voting across redundant channels (for example two-out-of-three), and an explicit choice between fail-safe (go to a known safe state) and fail-operational (keep working in a degraded mode).
4. Design for determinism: no dynamic allocation after start-up, bounded loops and queues, and fixed-priority scheduling with priority inheritance to avoid priority inversion. Isolate critical tasks from best-effort ones.
5. Verify the design: failure mode and effects analysis, fault-injection tests, static analysis and a restricted coding standard, and traceability from each requirement to the tests that cover it.

Follow-up

• How do you stop both redundant channels failing for the same reason?
• How would you detect a node that is still up but producing wrong values?
• What is priority inversion, and how does priority inheritance fix it?

What are the trade-offs between different database architectures (e.g., PostgresQL vs. in-memory stores) in hi

Medium · Technical & Domain Knowledge

What are the trade-offs between different database architectures (e.g., PostgresQL vs. in-memory stores) in high-throughput environments?

Approach

1. Start from the workload: the read/write ratio, the data size compared with RAM, the query shapes (key lookups or joins and range scans), and how many acknowledged writes the system can afford to lose.
2. PostgreSQL stores data on disk with a write-ahead log, ACID transactions, MVCC and B-tree indexes. Write throughput is limited by WAL flushes, which group commit and batching reduce. Setting synchronous_commit=off can lose the most recent commits after a crash but does not corrupt the database. Every connection is a backend process, so put a pooler such as PgBouncer in front at high concurrency.
3. An in-memory store such as Redis gives sub-millisecond key operations, and commands run one at a time, so a slow command blocks the others. The dataset is bounded by RAM. Durability comes from RDB snapshots or AOF, and with appendfsync everysec a crash can lose about a second of writes. Replication is asynchronous by default, so a failover can drop acknowledged writes.
4. Use them together and name the cost of each pattern. Cache-aside with TTLs keeps PostgreSQL as the source of truth. Write-behind to the cache is faster but risks losing data. Invalidation order matters, so delete from the cache after the database commit.
5. For high-rate append-only data, compare time-partitioned PostgreSQL tables or a time-series extension with a dedicated time-series store, and say which query becomes expensive under each choice.

Follow-up

• What exactly is lost if the Redis node crashes, under each persistence setting?
• How do you keep the cache consistent when two writers update the same row?
• At what point would you move write-heavy telemetry out of PostgreSQL?

Design a scalable backend service that handles real-time data from our aircraft sensors.

Medium · System Design & Architecture

Approach

1. Clarify scope and size it: how many aircraft and channels, the sample rates, whether data streams live or uploads after flight, and who consumes it (live monitoring, alerting, offline analysis). State the arithmetic as an assumption, for example 100 aircraft x 500 channels x 50 Hz = 2.5M samples/s, which is about 40 MB/s at 16 bytes per sample.
2. Ingest path: an authenticated gateway (mTLS per vehicle) writes to a durable partitioned log such as Kafka, keyed by aircraft id so each vehicle's samples stay in order. Buffer on the vehicle and replay when the link drops, and make writes idempotent by deduplicating on (aircraft, sensor, sequence number).
3. Processing: stream jobs validate ranges, convert units, downsample and evaluate alert rules. Use event timestamps from the vehicle with watermarks so late and out-of-order data is handled, rather than arrival time.
4. Storage: a time-series store keeps recent high-resolution data for dashboards, and columnar files such as Parquet in object storage hold full history for analysis. Retention tiers keep raw data for a short period and rollups for longer. A query API sits on top, and live views can be served over WebSocket or server-sent events.
5. Trade-offs and failure states: keying by aircraft makes fleet-wide queries scan every partition. Backpressure must protect the log, not drop samples silently. Consumer lag and the end-to-end delay from sensor timestamp to storage are the metrics to alert on.

Follow-up

• An aircraft is offline for an hour and then uploads its backlog. What happens to live dashboards and alerts?
• How do you make sure a threshold alert fires once, not once per retry?
• How would you change the design if sample rates went up tenfold?

How would you approach the deployment and orchestration of microservices using Kubernetes in a cloud-native en

Medium · System Design & Architecture

How would you approach the deployment and orchestration of microservices using Kubernetes in a cloud-native environment?

Approach

1. Packaging and delivery: one container image per service, built once and promoted between environments. Manifests are templated with Helm or Kustomize and applied through GitOps (Argo CD or Flux), so the cluster state matches a reviewed commit.
2. Rollouts: a Deployment with a rolling update (maxSurge / maxUnavailable). Readiness probes gate traffic, liveness probes restart hung processes, and startup probes cover slow boots. For risky changes, use a canary or blue-green release with automatic rollback on error-rate or latency metrics.
3. Resources and scaling: set CPU and memory requests so the scheduler can place pods. A CPU limit throttles the container, while exceeding the memory limit gets it OOMKilled. A HorizontalPodAutoscaler can scale on CPU or custom metrics, and PodDisruptionBudgets keep enough replicas up during node drains.
4. Lifecycle and configuration: handle SIGTERM with a preStop hook and a suitable terminationGracePeriodSeconds (30 s by default) so in-flight requests drain. Use ConfigMaps and Secrets, and remember that Secrets are only base64-encoded unless encryption at rest or an external secret manager is set up.
5. Isolation and observability: namespaces with RBAC, NetworkPolicies for service-to-service traffic, and metrics, logs and traces per service so a bad rollout is visible within one release.

Follow-up

• A new version passes readiness checks but errors climb. How does the rollout stop and roll back?
• How do you run a database schema migration alongside a rolling deploy without breaking old pods?
• A pod is repeatedly OOMKilled. How do you work out whether the cause is a leak or a wrong limit?

How do you manage infrastructure-as-code effectively in a distributed systems environment?

Medium · System Design & Architecture

Approach

1. State management: use a remote backend with locking and encryption for Terraform/OpenTofu state, never local state on a team. Restrict who can read it, because state can hold secrets. Split state by environment and component so one apply has a small blast radius.
2. Code structure: versioned modules for repeated patterns, thin per-environment root configurations, pinned provider and module versions, and secrets fetched from a secret manager instead of committed variable files.
3. Workflow: run plan in CI on every pull request and post the diff for review. Apply only from the pipeline after approval, never from laptops, and promote the same change through dev, staging and production in order.
4. Safety rails: lifecycle prevent_destroy on stateful resources, moved blocks or state mv for refactors so renames do not destroy and recreate resources, policy-as-code checks (for example OPA) on plans, and a scheduled plan that flags drift from manual console changes.
5. Trade-off: many small states reduce blast radius and lock contention but add cross-state dependencies through outputs or data sources. Say where you draw the boundary and why.

Follow-up

• Someone changed a security group by hand in the console. What happens on the next plan, and what do you do?
• How do you rename a module or resource without Terraform destroying the real infrastructure?
• How would you roll a risky change out across several regions or accounts?

Explain your process for debugging a complex system failure on a whiteboard.

Medium · System Design & Architecture

Approach

1. Pin down the symptom and its impact: what is failing, for which users or components, since when, and whether the failure is total, partial or intermittent. If impact is ongoing, mitigate first (roll back, turn off the flag, shed load) and diagnose afterwards.
2. Draw the request or data flow on the whiteboard and mark each hop as healthy, unhealthy or unknown, using metrics (rate, errors, duration, saturation), logs and distributed traces. The first unhealthy hop downstream of healthy ones is where to look.
3. Build a timeline of what changed: deploys, config, traffic shape, dependencies, certificates, clock or capacity changes. Then rank hypotheses and test first the one that rules out the most candidates whichever way the result comes out.
4. Cover the failure patterns specific to distributed systems: retry storms and cascading timeouts, a saturated shared dependency, partial network partitions, clock skew, and a single slow replica causing high tail latency.
5. Close the loop: confirm the fix with the same signal that showed the failure, add the missing alert or test, and write a blameless postmortem that separates the trigger from the underlying cause.

Follow-up

• The logs show nothing useful. What instrumentation do you add first?
• The failure is intermittent and does not reproduce in staging. What do you do next?
• How would you tell whether the recent deploy caused the problem or only coincided with it?

Suggested practice checklist

The plan starts with your own projects, then works through the reported systems questions, the case study and re-engineering questions, the battery and power-electronics questions, and finally the behavioral stories and a full rehearsal.

Day 1 — Map the loop and inventory your projects

• Write the three reported rounds in order and note which question types each is likely to exercise.
• For each project on your resume, write the constraint, the decision, two alternatives you rejected and one failure with its root cause.
• List what you must explain at depth in your language: Rust ownership and thread-safe sharing, or Python asyncio versus the GIL.

Deliverable: A one-page round map and a project sheet with constraint, decision, alternatives and failure for every resume project.

Day 2 — Sensor ingestion and pipeline latency

• Write clarifying questions for the real-time aircraft sensor backend: sensor count, message rate and size, ordering, loss tolerance, latency target and consumers.
• Sketch the path from device to gateway to a durable queue or log to stream processing to hot and cold storage, with the partition key and how you handle backpressure and duplicates.
• Take the AI-driven pipeline latency question and assign a latency budget to each stage, then list what you would measure and cut first.

Deliverable: One annotated diagram of the ingestion design and a per-stage latency budget with the top three optimisations.

Day 3 — Reliability and debugging

• Build a failure-mode table for the ingestion service covering crash, overload, partition, bad data and dependency outage, with detection, degradation and recovery for each.
• Write how you would test those failure responses before production.
• Rehearse a whiteboard debugging walk-through for a latency spike after a deploy: what changed, timeline, metrics, logs, traces, bisect, rollback, then root cause.

Deliverable: A failure-mode table and a one-page debugging flow, each spoken aloud once from memory.

Day 4 — Data stores, Kubernetes and infrastructure-as-code

• Write a comparison of Postgres and an in-memory store for high throughput: durability, query shape, write path, memory cost and recovery after restart.
• List Kubernetes rollout essentials: readiness and liveness probes, resource requests and limits, rolling updates, autoscaling and disruption budgets.
• Outline a Terraform or OpenTofu workflow with shared locked state, modules, separate environments and plan review in CI.

Deliverable: A Postgres versus in-memory comparison page, a Kubernetes rollout checklist, an infrastructure-as-code workflow outline and two solved practice questions.

Day 5 — Case study and re-engineering

• Invent a case prompt based on the sensor service and write a short design document: constraints, chosen design, two rejected alternatives and failure handling.
• Take a component you worked on and practise the re-engineering sequence: state the constraint, measure, find the bottleneck, change it, verify.
• Prepare a spoken answer on the hardest design decision in the case study, including what you would change now.

Deliverable: A short design document with rejected alternatives and a spoken answer for the hardest decision.

Day 6 — Domain questions and stakeholder stories

• Write a short primer on battery behavior for software: cells in series and parallel, state of charge, voltage sag under load, temperature effects and capacity fade over cycles.
• Write a short primer on DC/DC conversion: buck and boost, switching frequency trade-offs, efficiency, ripple and thermal limits.
• Write an honest answer about your power-electronics experience, then a STAR story for explaining a complex concept to a non-technical stakeholder and one for mentoring a junior engineer.

Deliverable: Two one-page primers, one honest experience answer and two STAR stories each ending with a concrete result.

Day 7 — Pressure, disagreement and full rehearsal

• Write STAR stories for a tight delivery schedule and for a technical disagreement, each with the trade-off made and the lesson learned.
• Run a full mock: present the case study, then answer follow-ups on trade-offs and failure handling.
• Rehearse the recruiter-style career walkthrough and fix the weakest answer from the mock.

Deliverable: Two finished STAR stories, one recorded mock presentation with follow-ups, and a revised career walkthrough.

Stories and domain answers for the Archer loop

Reported behavioral questions cover explaining technical ideas to non-technical people, mentoring, pressure and technical disagreement, and the loop also includes questions on your own experience and a case study walk-through. For this role, each story should show ownership from design to deployment, a decision with a stated constraint, and a result you can describe.

1. Walk me through the case study presentation you prepared; what was the most difficult design decision you faced?

Can you walk me through your experience with power electronics or DC/DC conversion?

Medium · Technical & Domain Knowledge

Approach

1. Treat this as an honest experience walkthrough. If you have hands-on experience, give the system, your own role, the converter or power stage involved, the constraint that mattered (efficiency, ripple, thermal limit, fault behavior) and how you verified the result.
2. If your experience is on the software side, say so plainly and describe the closest real work: firmware or services that read battery or power telemetry, fault handling, simulation, or test automation for power hardware. Bluffing about circuit design is easy to expose.
3. Show you can reason from basics. A DC/DC converter changes a DC voltage level by switching: a buck steps voltage down, a boost steps it up. Higher switching frequency allows smaller magnetics but adds switching loss and heat, so software that sets limits or reads faults needs to respect those trade-offs.
4. Finish by tying the experience to software: what the software had to monitor, which faults it detected, and what it did when a limit was crossed.

Follow-up

• What was the efficiency, ripple or thermal constraint, and how did you check that you met it?
• What fault did the software need to detect, and what did the system do when it happened?
• How would you test the software against power hardware that you could not run in every environment?

Describe a time you had to explain a complex technical concept to a non-technical stakeholder.

Medium · Behavioral & Leadership

Approach

1. Choose a concept tied to a decision, such as why a service needs redundancy or why a latency target changes cost. A story with a decision at the end is stronger than a story about teaching.
2. Open with what the listener had to decide and what they cared about (schedule, cost, risk), then explain the concept in those terms. Use one analogy and say where it stops being accurate.
3. Quantify in their units: for example, the chance and cost of an outage in place of a replication factor, or the days of delay in place of a refactor description.
4. Check understanding by asking them to restate the trade-off, and report the result: a decision made, a plan approved or a risk accepted knowingly.
5. The mistake that sinks the story is jargon with a thin analogy and no outcome, or a story where the audience simply agreed with you.

Follow-up

• How did you know they understood and were not just agreeing?
• What did you leave out on purpose, and why?
• What would you do if they had pushed back on your estimate or risk rating?

Tell me about a project where you had to mentor a junior engineer to drive a positive outcome.

Medium · Behavioral & Leadership

Approach

1. Name the junior engineer's starting point and the outcome that mattered to the team, such as owning a service component, shipping a feature or taking on-call. Make the outcome concrete.
2. Describe your method: scoping the task so it was hard but achievable, pairing early, giving code review feedback with reasons, and then stepping back as they gained ground.
3. Show you taught judgment, not just answers. Give one example where you let them choose and fail safely, and what you did afterward.
4. Report results in terms of what they could do alone by the end, and what you learned about mentoring.
5. The mistake that sinks the story is doing the work yourself and calling it mentoring, or claiming all the credit.

Follow-up

• What did you do when they disagreed with your review feedback?
• How did you balance delivery pressure against the time mentoring took?
• How did you measure that the mentoring had worked?

How do you handle high-pressure situations or tight delivery schedules?

Medium · Behavioral & Leadership

Approach

1. Choose a real deadline with a specific stake and say what was at risk. Vague busyness does not make a story.
2. Show triage: what you cut or deferred, who agreed to it, and which checks you refused to skip, such as tests on the failure-handling path. For safety-minded work, show that speed did not mean skipping verification of critical behavior.
3. Describe communication: you raised the risk early with numbers, offered options (reduce scope, add people, move date) and let the owner choose.
4. Close with the aftermath: what shipped, what debt you recorded and then paid down, and what you changed so the next crunch is less severe.
5. The mistake that sinks the story is a hero narrative about working overnight with no trade-off or lesson.

Follow-up

• What exactly did you cut, and who signed off on it?
• What would you do if the date and the quality bar both could not move?
• What changed afterward so the same pressure did not recur?

Describe a time you disagreed with a technical direction—how did you resolve it?

Medium · Behavioral & Leadership

Approach

1. Present both positions fairly. If you cannot state the other side well enough that its owner would agree, the story will sound like a complaint.
2. Say what the decision turned on (latency, operational cost, team skill, failure risk) and what evidence you used: a benchmark, a prototype, an incident review or a written comparison.
3. Describe how it resolved: one side convinced the other, you combined ideas, or you were overruled and committed to the decision. Each outcome is acceptable if you handled it well.
4. Give the result and what you learned, including a time your original view was partly wrong.
5. The mistake that sinks the story is making the other person wrong, or presenting an argument you won by seniority.

Follow-up

• What evidence would have changed your mind?
• If you had been overruled, how would you have carried out the decision?
• How did your working relationship with that person change afterward?