Autonomous Solutions Software Engineer Interview Guide

The 60-second brief

Candidates and the role listing describe Autonomous Solutions as a company that builds autonomous systems and robotics for heavy industries: self-driving vehicle platforms, perception systems and control frameworks for machinery that works outdoors and in industrial sites. A Software Engineer there writes code for perception, planning and control, refines state estimation and control loops, and builds simulation tools used to test them. The listing names C# and .NET among the technologies and describes daily work alongside mechanical, electrical, systems and validation engineers, with occasional support for field testing on real machines.

This guide is for software engineers interviewing at Autonomous Solutions, whether you come from computer science, robotics or electrical engineering. It covers the 4 reported rounds, the control theory and state estimation questions candidates describe (PID, transfer functions, the extended Kalman filter, nonlinear control), the coding and real-time design questions, the hardware and safety scenarios, and the four behavioral prompts. It does not cover pay, and it makes no claim about how any individual interviewer will behave.

Candidates report 4 rounds over roughly 3-5 weeks: a recruiter phone screen, a technical pre-screening, core interview rounds with senior engineers, and a final in-person panel with the hiring manager and team.

Focus areas

• PID Controllers
• Guidance, Navigation, and Controls (GNC)
• State Estimation

Overview

The work behind this role is software that sits on top of physical machines. Candidates and the role listing describe perception, planning and control code for autonomous heavy equipment, state estimation and control-loop refinement, simulation tooling, code reviews, and now and then support for field tests. That mix explains the shape of the loop: you are asked about control theory and sensor filtering as well as code, and you are expected to say what your software does to sensors, actuators and safety.

The 14 reported items fall into three groups. Six are domain questions: designing a controller and an estimator with an extended Kalman filter, how PID control and state estimation are used in vehicle navigation, deriving a model and transfer function, circuit design with fault analysis and safety standards, and filtering plus nonlinear control for robotic platforms. Four are programming questions: efficient solution design, performance-critical real-time architecture, and debugging edge cases in an embedded or automated system. Four are behavioral: cross-functional teamwork and on-site challenges, pressure during a live deployment, a technical conflict across engineering disciplines, and prioritising across projects. Two of the reported items are short statements of a theme rather than full questions, so prepare for them as topics.

Topic tags attached to the reported loops are PID controllers, guidance, navigation and controls, state estimation, extended Kalman filtering and hardware-in-the-loop testing. Less common areas that candidates mention are SLAM, reinforcement learning for planning, ISO 26262, CAN bus and low-level memory optimisation. The requirements candidates describe ask for object-oriented programming in a language such as C# or C++, and list .NET, ROS or ROS2, functional safety and hardware-in-the-loop validation as extras.

Plan for depth in one area, then breadth around it. Pick control, estimation or perception as the area you can discuss from first principles, with a project to point at, and keep the others at the level of explaining the trade-offs. The 7-day plan below spends two days on control and estimation, two on code and real-time design, one on hardware and safety, and ends with the behavioral stories and a full spoken run-through.

The Autonomous Solutions interview process

These are the stages candidates report, not a published process: exact steps and the number of rounds can change with the level and the team you interview for.

Recruiter Phone Screen

Candidates describe an opening call that goes through your resume, your project experience and how well you line up with the role. Treat it as the first technical filter as well as a logistics call: the recruiter hears your project history and decides what you can talk about in depth. Prepare a short, structured account of your work that points at the areas this role lists: planning, perception, control, state estimation, and C# or C++ development.

What to demonstrate

• How clearly you summarise your projects and your own part in them
• Whether your background lines up with the role's autonomy, robotics or complex-software focus
• Your language and tooling experience, such as C#, C++, .NET or ROS

How to prepare

• Write a two-minute walk through your career that ends with the reason you want autonomous heavy machinery work specifically, not just robotics in general.
• For your three strongest projects, note the sensors or hardware involved, the language, your decisions and one measurable result, so you can answer follow-ups without hunting for details.
• Prepare questions about the next stages, in particular whether the technical pre-screening is a coding test or a domain quiz, so you know what to drill.

Technical Pre-Screening

Candidates report an online programming evaluation or a specialised assessment on topics such as PID controllers and state estimation, with domain quizzes mentioned as a variant. Prepare for both formats: a coding test where efficiency matters more than a brute-force pass, and short conceptual questions on how PID control and state estimation are used in vehicle navigation and how a model and transfer function are derived.

What to demonstrate

• Working code in your chosen language with sensible time and space complexity
• Core control vocabulary: PID terms, transfer functions, stability, overshoot
• Basic state estimation ideas such as predict and update steps and sensor noise

How to prepare

• Do timed practice on streaming-data problems (sliding windows, ring buffers, monotonic queues) and state the brute-force cost and your target complexity out loud.
• Write a discrete PID controller by hand with an output clamp, anti-windup and derivative on the measurement, then explain what each choice protects against.
• Rehearse a two-minute answer on how PID loops and an estimator fit together in a vehicle: the estimator supplies position and heading, the controller turns the error into steering and speed commands.
• Be ready to derive a transfer function for a simple plant, such as a DC motor or a mass-spring-damper, on a blank page.

Core Interview Rounds

Candidates describe a technical deep-dive with senior engineers on models, filtering techniques and coding. The wider process description also mentions sessions with individual engineers and panels, and an emphasis on defending design choices. Expect to go from a plant model to a controller to an estimator, and to explain the maths behind each decision, then write or critique code for real-time use.

What to demonstrate

• Depth on modelling, transfer functions, loop tuning and stability
• Understanding of extended Kalman filtering and sensor fusion on noisy or intermittent data
• Coding quality and the ability to reason about threads, latency and memory
• Electrical and fault-analysis awareness at the level a software engineer needs

How to prepare

• Write a 2D extended Kalman filter with a nonlinear measurement, derive the Jacobians by hand, and test what happens when measurements drop out or arrive late.
• Prepare an answer on which filtering techniques and nonlinear control methods you would reach for and why: complementary filter, extended or unscented Kalman filter, particle filter, feedback linearisation, sliding mode, model predictive control.
• Sketch the threading design for a module that reads high-frequency sensor updates and drives a fixed-rate control loop, naming where locks, queues or latest-value buffers go.
• Review one electrical-systems topic from your own history, such as sensor interfaces, a bus like CAN, or a fault you traced, and be able to describe the failure modes and the safeguards.

Final Panel Interview

Candidates report an in-person interview with the hiring manager and the team that mixes technical scenarios with behavioral questions. This is where the physical side of the job matters most, so prepare scenario answers that connect software to sensors, actuators and safety, plus stories about working with electrical, mechanical, systems and validation colleagues.

What to demonstrate

• How you reason through a technical scenario involving hardware and software together
• Communication with a mixed audience of software and non-software engineers
• Concrete evidence from past work on collaboration, pressure and prioritisation

How to prepare

• Rehearse three troubleshooting sequences out loud: erratic steering traced to a sensor fault, a safety interlock for heavy machinery, and validating a planning or perception change before it runs on a test vehicle.
• Write four stories with a situation, your decision, the evidence and the result, one for each reported behavioral prompt, and practise telling each in under two minutes.
• Prepare questions for the team about field testing, simulation and hardware-in-the-loop validation, which the role listing mentions.

Where candidates lose points

Describing PID as proportional, integral and derivative terms without being able to derive or tune anything

Practise from a plant model. Derive the transfer function of a DC motor or mass-spring-damper, close the loop with a PI controller, and predict how each gain changes overshoot, rise time and steady-state error. Be ready to explain integrator windup and why derivative action amplifies sensor noise.

Treating the extended Kalman filter as a black box

Write the predict and update steps yourself, derive the Jacobians for a nonlinear measurement, and be able to say what the covariance represents. Know what you do when measurements drop out (keep predicting, let the covariance grow, gate outliers with the innovation) and how tuning the process and measurement noise changes the behaviour.

Giving a brute-force or allocation-heavy solution to a streaming, performance-critical coding problem

State the naive cost, then choose a structure from the access pattern: a ring buffer for a fixed window, a monotonic deque for sliding extremes, a latest-value buffer between threads. Mention memory use and avoiding allocation in a hot loop, which matters in C# as well as C++.

Answering design and debugging questions as if the software ran in isolation

For each answer, add what happens when a sensor goes stale, stuck or out of range, what the safe state is, and how a watchdog or interlock acts. Then say how you would validate the change in simulation or hardware-in-the-loop before it reaches a physical vehicle.

Telling behavioral stories in the plural, with no sign of your own decision and no technical detail

Use "I" for what you decided, name the other discipline and its constraint, give the evidence you used to settle the issue (a test, logged data, a simulation) and finish with a result and a lesson. Rehearse all four reported prompts aloud before the final panel.

Questions & practice

These questions evaluate your ability to write clean, efficient code and solve algorithmic challenges under co

Medium · Programming and Problem Solving

These questions evaluate your ability to write clean, efficient code and solve algorithmic challenges under constraints.

Approach

1. Confirm the constraints before coding: input size, value ranges, whether the data arrives as a stream or all at once, and any memory limit. These decide the target complexity.
2. Give the brute force and its cost, then improve it with the right pattern: a hash map for O(1) average lookup, two pointers or a sliding window over sorted or contiguous data, a heap for top-k in O(n log k), and BFS, Dijkstra or A* with an admissible heuristic for grid or graph paths.
3. For streaming sensor data under memory limits, keep fixed-size state: a ring buffer for the last N samples, Welford's algorithm for running mean and variance, and a monotonic deque for a sliding-window max or min in O(n) total.
4. Write clean code in your chosen language (C# or C++ match the requirements candidates describe for this role): clear names, small functions and no hidden allocation in the loop. State the time and space complexity when you finish.
5. Test the edge cases aloud: empty input, a single element, duplicates, extreme values with integer overflow, and a window larger than the input.

Follow-up

• The data no longer fits in memory. What changes in your solution?
• Can you make it work in one pass over the stream?
• What is the worst-case input for your approach, and how does it behave?

How would you approach designing a programming solution for a complex problem to ensure maximum efficiency?

Medium · Programming and Problem Solving

Approach

1. Pin down what efficient means for this problem: latency, throughput, memory or all three. Get the expected input size too: at n = 100,000, an O(n^2) loop is about 10^10 steps, which rules it out.
2. Fix the algorithm before tuning code. Pick the complexity class from the access pattern: a hash map for membership, a balanced tree or sorted array for ordered queries, a heap for repeated min or max, and precomputation or caching for repeated sub-results.
3. Then reduce constant factors where profiling shows the time goes: contiguous arrays over linked structures for cache locality, and no allocation in hot loops (in C#, Span<T>, ArrayPool<T> and structs; in C++, reserve() and stack buffers).
4. Keep correctness first. Write tests, including edge cases, before optimising, and measure every change with a benchmark harness (BenchmarkDotNet or Google Benchmark) rather than by intuition.
5. State what you traded: memory for speed in a cache or lookup table, and readability against a hand-optimised hot path. Optimise only the part that measurably matters.

Follow-up

• The profiler shows 80 percent of the time in one function. What do you check first?
• When would you accept a slower algorithm for simpler code?
• How do you stop the optimisation regressing later?

Find overlapping job attempts and peak concurrency from lease records

Medium · sweep line · intervals · leases

A day of job_run history yields about 50,000,000 attempt records: (job_run_id, job_type, attempt, started_at, finished_at which is NULL when the worker died, lease_expires_at). Leases expire on a clock, so a job that outran its lease ran twice. Produce (a) every job_run_id whose attempts overlapped in wall-clock time and (b) the peak number of simultaneously running attempts per job_type with the minute it occurred. Target O(n log n). State how you treat a NULL finished_at and what clock skew does to your answer.

Approach

1. Define the interval before sorting anything: an attempt occupies [started_at, COALESCE(finished_at, lease_expires_at)). finished_at is observed and lease_expires_at is only a promise, so every attempt without a finish contributes an estimate and the whole result is a lower bound on overlap rather than an exact count.
2. For peak concurrency, sweep: emit 2n endpoints, sort by (timestamp, kind) with ends ordered before starts at equal timestamps, then walk the sequence maintaining a counter per job_type and record each type's maximum with its timestamp. O(n log n) dominated by the sort, O(n) space, or O(1) extra if the sort is external and the walk streams.
3. For overlap detection, do not compare attempts pairwise. A single global sort by (job_run_id, started_at) gives both the grouping and the order; within a group, keep the maximum end seen so far and report an overlap exactly when the next start is less than that running maximum, which is one linear pass after the sort.
4. Half-open intervals matter and are easy to get wrong: with closed intervals an attempt ending at the same millisecond another begins reads as concurrency two, and across 50,000,000 records that artefact swamps the real signal.

Follow-up

• A handler is not idempotent and you have found 400 overlapping jobs. Which of them actually caused damage, and what would you query to find out?
• Peak concurrency for one job_type is 4 against a configured cap of 4. Is the cap working, or is the data hiding attempts that never started?

Denormalise tenant onto revisions and backfill it live

Hard · denormalisation · online migration · backfill

resource_revision (revision_id, resource_id, version, actor_user_id, change_kind, patch, request_id, created_at) has 400M rows and no tenant column; tenant_id lives only on resource. Two reads need it: a tenant-scoped audit feed ordered by created_at DESC, and an offboarding purge. Both join back to resource today. Justify adding tenant_id to resource_revision against those two reads, name the anomaly the copy introduces and the constraint that prevents it, then give the ordered migration for a live table taking 1.2k writes/second — the lock each step takes, how the backfill is batched, and where each step stops being reversible. PostgreSQL 16.

Approach

1. Justify from the access path rather than from taste. Without the column, the audit feed either scans resource_revision by created_at and discards other tenants' rows, or resolves the tenant's resource_ids first and probes with them — both proportional to the tenant's whole history rather than to one page. With (tenant_id, created_at DESC, revision_id DESC) it is a seek that stops at 50 rows, and the purge becomes a ranged delete instead of a join.
2. Name the cost exactly: a second copy of a fact can disagree with the first. Make the disagreement unwritable rather than documented — add UNIQUE (resource_id, tenant_id) on resource so it can serve as a foreign-key target, then FOREIGN KEY (resource_id, tenant_id) REFERENCES resource (resource_id, tenant_id) on the revision table. A revision can then only ever carry its parent's tenant.
3. Step one, expand: ALTER TABLE resource_revision ADD COLUMN tenant_id BIGINT NULL, with no default, so it is a catalogue change and no rewrite. It still needs ACCESS EXCLUSIVE for an instant, and that instant queues behind the longest open transaction on the table while every later query queues behind it — set lock_timeout to 2s and retry rather than wait.
4. Step two, dual-write: deploy the writer that populates tenant_id on every new revision while reads still use the join. Reversible by redeploying the previous build, because nothing reads the column yet.

Follow-up

• The backfill is half finished and a rollback is required. What state is the table in, and what does the previous build do with a half-populated column?
• How do you verify the backfill actually finished, given rows are still being inserted while it runs?

Find version gaps and relay lag with window functions

Medium · window functions · event tables · frame semantics

outbox_event holds event_id, aggregate_type, aggregate_id, aggregate_version, event_type, payload, status ('pending','published','dead'), attempts, created_at, published_at. A projection is missing rows and you must decide whether the relay skipped events or the consumer dropped them. Write three queries over the last seven days: one listing every aggregate_id whose published aggregate_version sequence has a hole, one giving per-day counts with a running total, and one returning the newest published event per aggregate. For each, say where the window function is evaluated relative to WHERE and LIMIT. PostgreSQL 16.

Approach

1. Gaps: compute lead(aggregate_version) OVER (PARTITION BY aggregate_id ORDER BY aggregate_version) in a subquery, then filter next_version <> aggregate_version + 1 in the outer query. Window functions are evaluated after WHERE, GROUP BY and HAVING and before the outer ORDER BY and LIMIT, so the predicate cannot sit in the same WHERE clause and PostgreSQL 16 has no QUALIFY.
2. Say what the seven-day filter does to the answer: it truncates every partition, so the first row per aggregate has no predecessor inside the window and a hole spanning the boundary is invisible. Widen the window, or join to resource.version as the authority for the true maximum.
3. Running total: SELECT date_trunc('day', created_at) AS d, count() AS n, sum(count()) OVER (ORDER BY date_trunc('day', created_at) ROWS UNBOUNDED PRECEDING). An aggregate inside a window call is legal because grouping runs before windowing. The grouping key is unique per row here so ROWS and RANGE agree, but write the frame anyway — over ungrouped rows with tied timestamps the default RANGE frame pulls in every peer row and the total jumps.
4. Newest per aggregate: DISTINCT ON (aggregate_id) ... ORDER BY aggregate_id, aggregate_version DESC is the cheap PostgreSQL-only form when an index matches that order; row_number() OVER (PARTITION BY aggregate_id ORDER BY aggregate_version DESC) = 1 is the portable form and needs a subquery for the same evaluation-order reason as the gap query.

Follow-up

• Relay failover redelivers events. Does a duplicate break the gap query, and how would you detect one from this table alone?
• Turn the gap check into a continuous monitor rather than a query someone runs after an incident. What does it watch?

Questions in this category test your foundational knowledge in control theory, state estimation, and electrica

Medium · Technical and Domain Expertise

Questions in this category test your foundational knowledge in control theory, state estimation, and electrical or systems engineering principles.

Approach

1. Treat this as a fundamentals sweep across three areas and give a crisp definition with a usable rule for each: control theory, state estimation and electrical basics.
2. Control: a continuous linear system is stable when every pole has a negative real part. A discrete system is stable when every pole lies inside the unit circle. Gain and phase margin, read from a Bode plot, show how far the loop is from instability.
3. State space: controllability means the inputs can steer every state, and observability means the outputs reveal every state. Check each with the rank of its matrix ([B AB ...] and [C; CA; ...]) before designing a controller or an estimator.
4. Estimation: the Kalman filter is the minimum-variance linear estimator for a linear model with Gaussian noise. Q sets how much the model is trusted and R how much the sensor is trusted. The EKF linearises a nonlinear model with Jacobians.
5. Electrical and systems basics: Ohm's and Kirchhoff's laws, the RC time constant tau = RC, and Nyquist sampling, which requires an anti-aliasing filter below half the sample rate. Tie each one to a sensor or actuator you have worked with.

Follow-up

• Your plant has a pole at s = +2. What does a step response look like, and how would feedback fix it?
• Which state in a GNSS-plus-IMU vehicle model becomes unobservable while the vehicle is stationary?
• What happens to a control loop if the sensor is sampled below twice its noise bandwidth?

How would you design a controller and an estimator using an extended Kalman filter for a dynamic system?

Medium · Technical and Domain Expertise

Approach

1. Write the model first: x_k+1 = f(x_k, u_k) + w with process noise covariance Q, and z_k = h(x_k) + v with measurement noise covariance R. Pick the states you need, for example position, heading and velocity for a ground vehicle.
2. Predict step: x- = f(x, u) and P- = F P F^T + Q, where F is the Jacobian of f at the current estimate. Update step: K = P- H^T (H P- H^T + R)^-1, x = x- + K (z - h(x-)), P = (I - K H) P-. The Joseph form keeps P symmetric and positive definite under rounding.
3. Controller: run an LQR on the model linearised about the operating point, or a PID on the estimated states, and feed it the EKF output. The separation principle guarantees the combination only for linear Gaussian systems (LQG), so check closed-loop behaviour in simulation for a nonlinear plant.
4. Handle real data. When a measurement is missing, run predict only, so the covariance grows honestly. Wrap angle residuals to [-pi, pi]. Gate outliers using the Mahalanobis distance of the innovation.
5. Tune and validate. Set R from sensor datasheets or logged static data, then adjust Q until the normalised innovation squared stays consistent with its chi-square bound. Mention a UKF when the Jacobians are hard to derive or the linearisation error is large.

Follow-up

• GNSS drops out for 30 seconds. What happens to P and to the controller, and how would you limit the drift?
• How do you choose Q when you have no ground truth?
• When would you switch from an EKF to a UKF or a particle filter?

Can you explain how PID controllers and state estimation techniques are applied in autonomous vehicle navigati

Medium · Technical and Domain Expertise

Can you explain how PID controllers and state estimation techniques are applied in autonomous vehicle navigation?

Approach

1. Outline the chain first. A planner produces a path and a speed profile. An estimator fuses GNSS, IMU and wheel odometry, typically with an EKF, into pose and velocity. Controllers then turn tracking error into steering and throttle or brake commands.
2. Longitudinal control: a PID on speed error. P responds to the present error, I removes steady-state error such as a constant grade, and D adds damping. Add anti-windup (clamping or back-calculation) because throttle and brake saturate.
3. Lateral control: a PID on cross-track and heading error works at low speed. Pure pursuit or Stanley handles path geometry better. Gain-schedule with speed, because the same gain that is stable at low speed oscillates at high speed.
4. Implementation details: run at a fixed rate with the real dt, take the derivative of the measurement rather than the error to avoid a kick when the setpoint steps, and low-pass the derivative term because it amplifies sensor noise.
5. Show where estimation quality limits control. Estimator latency adds phase lag, a jump in the position fix appears as a step in cross-track error, and the controller can only be as smooth as the state it is fed.

Follow-up

• The vehicle overshoots after climbing a slope and cresting. What is happening, and how do you fix it?
• How would you tune the speed loop on a real machine safely?
• Which state estimate would you trust if GNSS and wheel odometry disagree?

What steps are involved in deriving a model and transfer function for classical control theory applications?

Medium · Technical and Domain Expertise

Approach

1. Define the inputs, the outputs and the operating range. Then write the governing equations from physics: Newton's second law for mechanical parts and Kirchhoff's laws for circuits.
2. Worked example: a mass-spring-damper, m x'' + c x' + k x = F, gives G(s) = X(s)/F(s) = 1/(m s^2 + c s + k) under the Laplace transform with zero initial conditions. A DC motor gives omega/V = Kt / ((J s + b)(L s + R) + Kt Kb).
3. If the model is nonlinear, linearise it with a first-order Taylor expansion about an equilibrium point. Say that the transfer function only holds near that point.
4. Analyse the result: poles and zeros, natural frequency and damping ratio, DC gain and step response. Add any transport delay as e^(-sT), using a Pade approximation when you need a rational form.
5. Validate and implement. Compare the model's step response with logged data or fit parameters by system identification. Then discretise it for the controller with zero-order hold or Tustin (s = (2/T)(z-1)/(z+1)).

Follow-up

• How would you estimate m, c and k if you could only apply inputs and log the response?
• What does a right-half-plane zero do to the step response, and why does it limit controller gain?
• How large can the deviation from the operating point get before the linear model stops being useful?

How do you approach circuit design, fault analysis, and safety standards implementation in complex electrical

Medium · Technical and Domain Expertise

How do you approach circuit design, fault analysis, and safety standards implementation in complex electrical systems?

Approach

1. Start from the hazards rather than the circuit. List what can go wrong, how severe each outcome is and how you would detect it. FMEA works bottom-up, scoring severity times occurrence times detection. Fault tree analysis works top-down from one hazardous event.
2. Design-level protections to name: a fuse or current limit on every supply rail, reverse-polarity and TVS protection on inputs, a sound grounding scheme, and current or voltage sensing so that faults are observable.
3. Software's part: a watchdog that forces a safe state, heartbeat timeouts between nodes, plausibility checks on sensors (range, rate of change, cross-checks between redundant sensors), and a defined safe state such as a controlled stop. Keep the emergency stop hardwired and independent of software.
4. Standards: name the one you have actually worked under, such as ISO 26262 for road vehicles or IEC 61508 for general functional safety. Explain how its integrity level drove redundancy, diagnostics coverage and traceability from requirements to tests.
5. Prove the fault paths work. Use fault injection (open circuit, short, stuck value, lost message) in hardware-in-the-loop testing, and have a test record for every safety requirement.

Follow-up

• A wheel-speed sensor reports a constant value while the vehicle moves. How do you detect it, and what does the system do?
• For a heavy machine, when is cutting power the wrong safe state?
• How do you show that a safety mechanism still works after a software change?

What filtering techniques and nonlinear control methods do you rely on for stabilizing dynamic robotic platfor

Medium · Technical and Domain Expertise

What filtering techniques and nonlinear control methods do you rely on for stabilizing dynamic robotic platforms?

Approach

1. Filtering, from simple to complex: a first-order low-pass (exponential moving average) for noise, a median filter for spike outliers, a complementary filter that fuses high-passed gyro with low-passed accelerometer for attitude, and a Kalman, EKF or UKF when a model and noise statistics are available.
2. State the trade-off: heavier smoothing adds phase lag, and lag inside a feedback loop erodes phase margin and can destabilise it. Use the filter cutoff and delay to justify that the loop still has margin.
3. Nonlinear control options: gain scheduling across operating points, feedback linearisation when the model is well known, sliding mode control for robustness to bounded disturbances (replace the sign function with a saturating boundary layer to reduce chattering), and MPC when actuator and state constraints matter.
4. Support the stability claim with a Lyapunov function where you can find one, then check it in simulation across parameter variation and on hardware-in-the-loop before running the real platform.
5. Close with one platform you stabilised: what was nonlinear (saturation, friction, varying load), which method you chose, and what you measured afterwards.

Follow-up

• Your low-pass filter fixed the noise, but the loop now oscillates. Why, and what would you change?
• When is MPC too expensive to run at your control rate, and what would you use instead?
• How do you handle actuator saturation inside a sliding mode or feedback-linearising controller?

What considerations do you make when optimizing code architecture for performance-critical real-time applicati

Medium · Programming and Problem Solving

What considerations do you make when optimizing code architecture for performance-critical real-time applications?

Approach

1. Lead with determinism: in a control loop, worst-case latency and jitter matter more than average throughput. Budget the execution time of each stage against the loop period, and measure p99 and maximum, not the mean.
2. Remove unbounded work from the hot path: no dynamic allocation after startup, no blocking I/O and no unbounded queues. In .NET, avoid GC pauses by preallocating buffers and pooling. GCSettings.LatencyMode = SustainedLowLatency or a no-GC region can help but does not replace avoiding allocation.
3. Concurrency: split work by rate, with a fast control thread and slower planning and logging threads, and pass data through lock-free single-producer single-consumer ring buffers or double buffering. Where locks remain, use priority-inheritance mutexes to avoid priority inversion.
4. Timing hygiene: timestamp sensor data at acquisition using a monotonic clock, use the real dt in calculations, and detect and log missed deadlines instead of silently running late.
5. Architecture: keep modules (estimation, control, planning) behind narrow message interfaces, such as ROS 2 topics if you have used them. Then a slow component degrades only its own rate and does not stall the control loop.

Follow-up

• The control loop misses a deadline. What should the code do in that cycle?
• How would you make a sensor-update module thread-safe without adding latency?
• How do you measure jitter on the target hardware?

Can you walk through your process for debugging unexpected edge cases in an embedded or automated system?

Medium · Programming and Problem Solving

Approach

1. Reproduce first. Capture timestamped logs and raw sensor recordings (for example ROS bags) from the event so the failure can be replayed in simulation or on hardware-in-the-loop, instead of debugging on the machine in the field.
2. Check the inputs before the logic: units, coordinate frames, timestamps and clock sync, sensor validity flags, and whether the logging itself dropped or reordered messages.
3. List the usual suspects for edge cases in automated systems and test them one by one: angle wrap-around at plus or minus pi, integer overflow or float precision, uninitialised state at startup or after a reset, race conditions between threads, and stale data after a sensor dropout.
4. Narrow it down systematically. Bisect by replaying recorded data through subsets of the pipeline, or by bisecting commits. Add targeted logging around the suspect state, and watch for timing changes that hide a race.
5. Close the loop: fix the root cause rather than the symptom, add the recorded case as a regression test, and confirm on hardware-in-the-loop before running it on the vehicle again.

Follow-up

• The bug appears only in the field and disappears when you add logging. What does that suggest, and what do you do?
• How would you debug it if you could not record raw sensor data?
• What would you change so this class of bug is caught before deployment?

Suggested practice checklist

Built around the reported rounds: control and estimation come first because the technical pre-screening and deep-dive both cover them, then efficient code, real-time design and debugging, hardware safety, and finally spoken behavioral practice.

Day 1 — Map the loop and find the gaps

• Write the 4 reported rounds in order and note what each covers: the recruiter call on your background, the online coding or PID and estimation assessment, the senior-engineer deep-dive, and the in-person panel.
• Rate yourself from 1 to 5 on transfer functions, PID tuning, extended Kalman filtering, threading in C# or C++, embedded debugging, fault analysis and behavioral stories, then rank your three weakest.
• Choose the one language you will code in for the assessment and the deep-dive, and list three projects you can discuss in depth.

Deliverable: A one-page loop map, a skills grid with the three weakest topics ranked, and the language and three projects chosen.

Day 2 — Transfer functions and PID in vehicle navigation

• Derive the transfer function of a DC motor from voltage to speed, using the electrical and mechanical equations and the Laplace transform, then close the loop with a PI controller and check stability from the poles.
• Write a discrete PID controller with an output clamp, anti-windup and derivative on the measurement, and simulate a step response with two gain sets to see the overshoot change.
• Record a two-minute spoken answer on how PID loops and state estimation work together in vehicle navigation.

Deliverable: A one-page derivation, a runnable PID with step-response output for two gain sets, and a recorded spoken answer.

Day 3 — Extended Kalman filter and nonlinear control

• Write a constant-velocity Kalman filter, then extend it to an extended Kalman filter with a nonlinear range-and-bearing measurement, deriving the Jacobians by hand.
• Add a test with dropped measurements and show the covariance growing during the gap, then add innovation gating to reject outliers.
• Prepare a spoken answer on designing a controller and an estimator together, and a comparison table of filters and nonlinear control methods (complementary, unscented, particle, feedback linearisation, sliding mode, model predictive control) with one trade-off each.

Deliverable: A working filter with a dropout test, and a one-page table of filtering and nonlinear control options with trade-offs.

Day 4 — Efficient code under constraints

• Solve three timed problems on streaming sensor data in your chosen language: a fixed-window moving average with a ring buffer, a sliding-window maximum with a monotonic deque, and a bounded-memory running statistic.
• For each, say the brute-force cost, the target time and space complexity, and the edge cases (empty window, repeated values, window larger than the stream).

Deliverable: Three tested solutions with their complexity written beside them, plus the completed practice exercise.

Day 5 — Real-time architecture and debugging embedded edge cases

• Sketch a multi-threaded module that takes high-frequency sensor updates and runs a fixed-rate control loop: producer thread, latest-value buffer or queue, timestamps, no allocation inside the loop, and where each lock or atomic goes.
• Write a step-by-step plan to refactor a legacy control loop: separate input and output from the estimator and controller, record real data for replay, add regression tests, then change the structure.
• Write your debugging checklist for unexpected edge cases in an embedded or automated system: reproduce from logs, check timestamps and units, look for saturation, overflow and NaN values, and test sensor dropouts.

Deliverable: An architecture diagram, a refactor plan and a one-page debugging checklist.

Day 6 — Hardware integration, fault analysis and safety

• Build a failure-mode table for a steering sensor chain: stale reading, stuck value, out-of-range value and two sensors disagreeing, with the detection method and the safe response for each.
• Design a software safety interlock for a heavy machine, covering watchdog, emergency stop behaviour and what the system does when commands stop arriving.
• Write a validation ladder for a planning or perception change: unit tests, simulation, hardware-in-the-loop, closed-course test vehicle, with the exit criterion for each step.
• Prepare a short answer on circuit design, fault analysis and safety standards from a software engineer's viewpoint, naming CAN bus and ISO 26262 as topics to read about.

Deliverable: A failure-mode table, an interlock design and a validation ladder, plus a spoken answer on electrical faults and safety standards.

Day 7 — Behavioral stories and a full spoken run-through

• Write one story for each reported behavioral prompt: cross-functional work and on-site challenges, a high-pressure live deployment, a technical conflict across disciplines, and prioritising across projects. Use "I" for each decision and include the evidence and result.
• Practise each story aloud in under two minutes and record yourself, then remove any "we" that hides your part.
• Run a mixed mock: one derivation, one extended Kalman filter explanation, one coding problem and one hardware troubleshooting scenario, talking through your assumptions as you go.

Deliverable: Four written and recorded stories, plus a self-scored mock with a short list of fixes.

Stories for hardware and software teams

The reported behavioral questions focus on working across engineering disciplines: teamwork and communication with electrical, mechanical, systems and validation colleagues, staying effective when a live deployment goes wrong, settling a technical conflict, and juggling several projects. For this role, choose stories where software met hardware or field conditions, and show your own decision, the evidence behind it and the result.

1. Tell me about a time a hardware or safety constraint made you change a software design.

Interviewers use these scenarios to assess how you handle cross-functional teamwork, communication, and real-w

Medium · Behavioral and Collaboration

Interviewers use these scenarios to assess how you handle cross-functional teamwork, communication, and real-world on-site challenges.

Approach

1. This reported item is a theme rather than a single prompt: cross-functional teamwork, communication and on-site challenges. Prepare one story that touches all three, a project where your software had to work with another discipline's hardware or test process.
2. Open with two sentences of context: the system, the other teams involved (electrical, mechanical, validation) and your exact responsibility. Spend the remainder on what you decided and why.
3. Show translation. Explain how you put a software constraint such as loop rate, latency budget or message timing into terms for people who think in volts, torque or test procedures, and what you changed in an interface document or checklist so the misunderstanding could not recur.
4. If the on-site element is real, say what the field showed that the desk or simulation did not, the change you made and a measurable result such as fewer faults or a repeatable test setup.
5. The story fails when it is told entirely in terms of what the team did, so your contribution is invisible, or when it blames the other discipline.

Follow-up

• What did the other team need from you that you had not expected?
• How did you confirm the fix on the real hardware rather than only in simulation?
• What would you change about how you communicated at the start?

How do you handle high-pressure situations or unexpected roadblocks during a live deployment?

Medium · Behavioral and Collaboration

Approach

1. Pick a real incident during a deployment, release or test day where something failed or blocked progress, and state the stakes in one sentence. A story where pressure was only a tight deadline is weaker than one with a live failure.
2. Show the order of your actions: stabilise first (roll back to the last known-good build, stop the test, put the machine in a safe state), then diagnose. With heavy equipment, safety comes before schedule, and saying so explicitly is part of the answer.
3. Describe the diagnosis under pressure: what you checked first (logs, version diff, configuration, sensor health), the hypothesis you eliminated, who you pulled in, and how you kept the people waiting on you informed.
4. Close with the aftermath: the root cause, the fix, and the check, test or runbook entry you added. Say what you would do differently.
5. The mistake that sinks it is a heroics story: working alone, skipping the review or rollback process, and presenting the long hours as the achievement.

Follow-up

• When did you decide to roll back instead of continuing to debug, and who made that call?
• What did you tell the people waiting on the result while you were still diagnosing?
• What changed afterwards so the same failure would be caught before deployment?

Can you describe a time when you collaborated with multidisciplinary engineering teams to resolve a technical

Medium · Behavioral and Collaboration

Can you describe a time when you collaborated with multidisciplinary engineering teams to resolve a technical conflict?

Approach

1. Choose a conflict that was technical, with two defensible designs and different costs: filtering a signal in hardware or in software, sampling rate against bus bandwidth, or who owns the timeout and safety logic. Personality clashes make a weak answer.
2. Present each side in its own terms before giving your view: electrical cares about power and noise, mechanical about actuator limits and tolerances, validation about repeatable tests, software about latency and maintainability. Showing you understood their constraint is half the answer.
3. Explain how it was resolved with evidence rather than seniority: a bench test, a simulation or hardware-in-the-loop run, logged field data or a small prototype. State what the data showed, the decision, and who made it.
4. Name the lasting result, such as an interface specification, a shared test or a clear owner for the disputed logic. If you gave way on your original position, say so, because it shows judgement.
5. The mistake is a story in which you were right and everyone eventually agreed, with no trade-off acknowledged.

Follow-up

• What evidence would have changed your mind?
• How did you keep the working relationship in good shape afterwards?
• What was left unresolved, and how did you handle it?

How do you manage your priorities when working across multiple project-based engineering initiatives simultane

Medium · Behavioral and Collaboration

How do you manage your priorities when working across multiple project-based engineering initiatives simultaneously?

Approach

1. Begin with an inventory, which most answers skip: the initiatives you were running, their deadlines and dependencies, and who was blocked on you. Use a real situation with competing commitments, such as feature work, a defect found in the field and support for a test event.
2. State your ranking rule and apply it: cost of delay and reversibility. Fixed windows (a field test slot, shared hardware availability) and safety-related defects come first, while flexible refactors can move.
3. Show negotiation. Tell stakeholders what slips and why and get agreement, rather than quietly dropping work. Mention the tools you actually used, such as a short written priority list or a weekly check-in.
4. Give specifics on the outcome: what shipped on time, what moved, what you declined, and how you protected uninterrupted time for the hardest task.
5. Answers that rely on working longer hours, or that call everything top priority without naming a trade-off, show no prioritisation at all.

Follow-up

• A field issue arrives mid-sprint: what do you drop, and how do you tell people?
• How do you handle two managers who each say their project is first?
• Describe a prioritisation call you got wrong and what you learned.