I run a technical recruiting firm that hires robotics talent for startups, OEMs, and operations teams inside very traditional plants. The best interviews are not a maze of puzzles. They feel like a clear technical conversation with guardrails, a simple design or debugging task that mirrors the work, and a scoring rubric that keeps everybody honest. This guide shares the questions I use by level, how we evaluate answers, and the practical exercises that separate strong candidates from great ones without burning a weekend on homework.
What Great Robotics Interviews Actually Measure
Robotics sits at the junction of software, hardware, and safety. If you only test code fluency or only test PLC familiarity, you miss the seam work that makes robots reliable in the real world. The research on selection is blunt about what predicts performance. Work sample tests and structured interviews are strong predictors, especially when you make the evaluation criteria explicit. The updated meta-analysis on selection methods shows high validity for structured and unstructured interviews when they are anchored to the job and scored consistently, with additional lift when combined with job knowledge or work samples. If you want the receipts, read the 100-year review by Schmidt and colleagues that quantifies predictive validity across methods, including interviews and job knowledge tests. For hands-on tasks, the meta-analysis of work sample tests is also clear. Properly designed samples that reflect the real job predict on-the-job performance better than trivia-style questions. In robotics, this translates to short, realistic exercises using sanitized logs, a stripped-down ladder program, or a small perception dataset, paired with a structured scoring key. When teams do this, signal quality jumps and bias falls because everyone is trained to the same target.
Calibrate Role Levels Before You Write a Single Question
Before you start drafting questions, write two short paragraphs per level that describe scope and impact. Early career engineers contribute to a well-defined subsystem and learn how your stack fits together. Mid-level engineers own a module end to end and handle common failure modes with minimal supervision. Senior engineers lead design across interfaces, coordinate with field teams, and change the way your group builds systems. Staff or principal engineers set patterns, pay down risk early, and mentor others.
When this is on paper, your interview questions become easier to draft. A junior perception candidate can be asked to explain camera models and basic calibration, then walk through a simple labeling and evaluation task. A senior robotics software candidate can be asked to design lifecycle nodes in ROS 2 for a multi-sensor pipeline and reason about QoS choices on a lossy wireless link. The level definition also tells you what not to ask. I often remove questions that demand deep GPU kernel optimization from mid-level roles that will never touch device code. Clarity saves calendar time and reduces false negatives.
Technical Questions for Robotics Software, With Rubrics
For generalist robotics software roles, I focus on messaging, state, timing, and the discipline of shipping code to real machines. I also ground questions in your distribution and support horizon. ROS 2 moved to a yearly release on May 23 and even-numbered releases are long-term support. That affects your dependency choices and your candidates’ answers about upgrade paths, so it belongs in the conversation.
Example question: “You are designing a perception pipeline that feeds a motion planner across a flaky wireless bridge. Explain how you would choose ROS 2 QoS profiles for depth images and detections, how you would structure lifecycle nodes for controlled start and stop, and how you would log enough data to reproduce a field failure without keeping every frame.”
Rubric: Full credit if the candidate selects appropriate QoS policies, for example, sensor_data for high-rate topics, reliability tradeoffs explained, and discusses history and depth choices. Strong answers mention lifecycle transitions, health checks, and deterministic shutdown. Excellent answers add a brief plan for log sampling, time sync, and a replay harness. Partial credit if the answer stays generic or skips the reality of bandwidth limits. Red flag if they do not know what QoS does in practice.
Example question: “A field unit reports periodic stalls during heavy logging. CPU looks healthy. How do you isolate whether this is callback starvation, executor configuration, or a file system bottleneck?”
Rubric: Full credit for a measured approach with metrics, profiling, executor choices, and back-pressure analysis. Good answers separate log IO from callback latency and outline a minimal reproduction. Red flag if the candidate jumps to rewriting the stack without diagnosing the bottleneck.
Technical Questions for Controls and Automation, With Rubrics
Controls interviews should feel like a real conversation on the shop floor. What matters most to me is whether a candidate can talk through safety, commissioning, and cycle time in practical terms, not whether they can recite exotic math. It helps to bring standards into the discussion early so everyone is grounded in the same context. ISO 10218 lays out the baseline requirements for industrial robot safety, and ISO/TS 15066 adds important guidance when the application involves collaborative operation. Framing questions with these standards in mind sets the right tone and quickly shows whether a candidate understands the realities of deploying robotics in production environments. Candidates who have shipped systems should know how to translate these into design and validation steps.
Example question: “You are retrofitting a pick-and-place cell on a brownfield line. The plant wants to run a collaborative mode near operators during setup, then run at full speed in production. Explain how you would design safety and mode switching, and outline the validation you would perform before FAT.”
Rubric: Full credit for describing a proper risk assessment, selection of protective measures, speed and separation monitoring or power and force limiting where appropriate, mode interlocks, and documented test procedures. Strong answers mention verification artifacts and traceability to the chosen standard. Red flag if they equate collaborative with cage-free or skip validation.
Example question: “Commissioning reveals a rare blocking state after a downstream jam clears. Walk me through your method to find and eliminate the deadlock.”
Rubric: Full credit for a clean state machine review, IO traces, and a plan to reproduce with the exact handshake. Good answers include clear fault recovery strategies and audit of timer ranges. Red flag if the candidate treats it as a mystery or guesses without an experiment.
Technical Questions for Perception and Applied ML, With Rubrics
Perception interviews are easy to turn into research debates. Resist that. Focus on data quality, calibration, deployment constraints, and post-deployment learning. I ask candidates to talk through dataset curation, bias, evaluation, and low-latency deployment on edge accelerators. Then I test whether they can talk about the messy parts. What happens when the floor is wet and reflective. How do you continue operating with partial sensor failure. When do you choose a simpler model and invest in better fixtures instead. For scoring, keep it anchored to field impact.
Example question: “You inherit a bin picking model that looks great in the lab and falls apart on reflective packaging. Describe your plan for data capture, labeling policy, model changes, and quick mitigations that do not require a new model.”
Rubric: Full credit for proposing targeted data collection in the problem domain, label consistency checks, augmentation aligned to physics, and a plan for on-robot evaluation. Excellent answers add quick mechanical mitigations like matte tray inserts and lighting control. Red flag if the solution is “more data” with no policy or only a bigger model.
Example question: “Your edge device has strict thermal and memory limits. Describe model selection and optimization steps that preserve accuracy and meet latency targets.”
Rubric: Full credit for quantization, pruning, TensorRT or similar toolchains, and measurement under real load. Good answers discuss batching limits and graceful degradation. Red flag if the candidate ignores hardware limits or measures only on a desktop.
Behavioral Signals That Predict Success in the Field
Technical depth matters. In robotics, the behavioral behaviors that keep pilots alive matter just as much. I use structured behavioral prompts tied to the job. That matters both for making accurate predictions and for keeping the process fair. Research, including the century-long review of selection methods, indicates that structured interviews are more reliable because they improve validity and reduce the noise that often creeps in when different interviewers use their own approach. I also pay close attention to the quality of the process itself. A sloppy or inconsistent structure can easily introduce bias or lead to uneven results, even when the questions themselves are good. Reviews of algorithmic hiring and selection practices show how unvalidated tools or poorly defined criteria can introduce bias into decisions. While most robotics teams do not use algorithmic scoring, the caution applies. Validate what you measure, use evidence, and stick to the rubric you published to the panel.
Behavioral prompt: “Tell me about a time a robot failed in front of a customer. What did you do in the moment, what changed after, and what did you write down so it would not happen again.”
Rubric: Full credit for calm incident response, root cause beyond the obvious, and a change that sticks, for example, an updated runbook, an alert, or a design change. Red flag if the story blames others without learning or shows disregard for safety.
Behavioral prompt: “Describe a conflict with another engineering group that affected a program. How did you create alignment and what trade did you accept.”
Rubric: Full credit for specific context, measured communication, and an example of an explicit trade. Red flag if the answer never names the trade or avoids ownership.
Practical Assessments That Resemble the Work
Work sample tests shine in robotics because they compress a slice of the job into a practical, time-boxed task. The literature supports their use when they are well designed and well scored. The meta-analytic review by Roth and colleagues details predictive validity and also cautions teams to mind design quality and scoring discipline. For pair or collaborative tasks, research on assessment strategies points to the value of explicit rubrics and a mix of self, peer, and facilitator inputs to avoid conflating group skill with individual capability. In practice, I use three patterns. First, a logs-and-diagnosis exercise for robotics software, built around a small bag file and a prompt to find the failure path. Second, a PLC review and light code change for controls, presented on a portable test rack or a simulated cell. Third, a small dataset curation and evaluation task for perception, with a strict time window and a request for a one-page write-up. None of these should require more than two hours. If the assignment would take a weekend, you are not testing judgment. You are testing free time.
Red Flags, And How To Probe Them Fairly
Most red flags come from patterns rather than isolated gaps. I watch for four. First, answers that ignore safety or treat validation as optional. Anchor your question to ISO 10218 and, if relevant, ISO/TS 15066. Ask the candidate to name the validation artifacts they would produce and the risks they would try to reduce first. Second, an allergy to measurement. Great engineers can name the metric that tells them they are winning. Third, magical thinking about bandwidth, compute, or commissioning windows. Strong candidates ground their plan in constraints. Fourth, an inability to speak plainly about a failure. Everyone has one. The question is how they learned and what changed. Probe each flag with a second scenario rather than a confrontation. You get cleaner signal and the candidate stays in problem-solving mode.
Onsite, Remote, And How to Split the Loop
Robotics is physical, so I do not try to run a fully remote loop. I split interviews between remote planning and onsite work. The planning phase is a video call with a whiteboard or shared doc, used to explore architecture and tradeoffs. The onsite phase is hands on, often with a log replay or a low-risk hardware station. The goal is not to watch someone type. The goal is to watch how they approach messy information, keep a notebook, and ask for small experiments before big changes. When we have done this well, candidates leave saying the process felt fair and real. That matters for acceptance rates as much as it matters for prediction. Structured interviews and work samples outperform clever puzzles, and they do so in a way that is easier to defend and repeat.
Rubric Design That Keeps Everyone Honest
A rubric does not need to be fancy. It needs to be written before the loop starts, tied to the job, and visible to every interviewer. I keep four anchors for each interview: technical depth, system thinking, safety and reliability awareness, and communication. Each anchor gets a description of what a one, three, and five looks like. Interviewers must write evidence, not adjectives. “Selected sensor_data profile and justified reliability tradeoffs” is evidence. “Smart communicator” is an adjective. For fairness, I also train interviewers to avoid drift. If the first candidate was excellent and the second is good, do not score the second as mediocre because of contrast. Score to the anchors. This is not only a philosophical point. The structured approach is what the meta-analyses say will improve prediction and reduce noise across interviewers.
Sample Question Sets You Can Reuse Tomorrow
Early career robotics software: Ask about time, state, and debugging. “Explain the difference between best effort and reliable delivery in ROS 2 and when you would choose each.” “How would you reproduce an intermittent crash that appears only after twenty minutes in the field.” Score for clarity, practical use of logs, and safe change plans. Include a twenty minute log-reading exercise and ask for a short note that lists suspected causes and the next experiment.
Mid-level controls: Ask about commissioning and error handling. “How do you design a state machine that can recover from a downstream jam without operator intervention.” Include a short ladder logic review with one intentional race. Score for precise reasoning, not syntax trivia. Ask for validation steps and operator training notes.
Senior perception: Ask about data, deployment, and change management. “Your model degrades after three months in a new facility. What do you measure first, what do you collect, and how do you ship a fix without halting production.” Score for concrete measurement, risk controls, and a plan that separates hotfix from long-term improvement.
Real-World Examples That Changed How I Interview
On a mobile manipulation search, our client had been asking for deep kernel optimization experience for a role that would run mostly on top of existing inference engines. We rewrote the loop to emphasize data discipline and deployability. The practical task asked candidates to propose an evaluation plan on a small, noisy dataset and to write a one-page plan to cut false negatives in half. The engineer who scored highest never touched CUDA, yet they shipped a better plan in a week than we had seen in months. They merged a tighter labeling policy with a small change to fixtures and lighting. That person went on to lead the post-deployment learning pipeline. The lesson was simple. Test what the job demands. Not what the internet tells you is cool.
On a brownfield controls project, we used a portable rack with the exact PLC family the plant ran. The exercise was not to code a full cell. It was to read a short program, spot the blocking state, and write a safer recovery. The hire who did this calmly in forty minutes later became the go-to for changeovers. The field team stopped dreading night shift because the person who wrote the fix also wrote the runbook. That is the kind of behavior your rubric should reward.
Putting Safety Inside the Questions, Not As a Separate Section
Robotics interviews often treat safety as a checkbox. Bring it into the core. If you are assessing a design decision for a collaborative application, ask how the candidate would document the risk assessment and what parts of ISO/TS 15066 they would lean on for power and force limits or speed and separation choices. If you are assessing an industrial robot installation, ask how they map design choices to ISO 10218 and what validation artifacts they would produce before FAT. The goal is not to turn engineers into auditors. It is to hear them reason about risk in the same breath as performance, which is what the standards are trying to drive in the first place.
How to Close Strong Candidates After a Fair Process
The best engineers judge you while you are judging them. Share the loop up front, publish the rubric to candidates in simple terms, and keep time demands reasonable. Do not assign a weekend project. Do not surprise candidates with a new round at the last minute. When we tightened loops this way, our acceptance rates rose. Candidates said the work felt like the job and the team felt like adults. That is the point. A good process predicts performance. It also sells your culture without a slide deck.
Bringing It All Together
Interviewing robotics engineers is not a guessing game. Decide what the job truly needs, then write questions and exercises that map to those needs. Keep rubrics short and written before you start. Mix structured conversations with small, realistic work samples. Bring safety and reliability into the center of the discussion rather than parking them at the end. Anchor your choices in research so your panel works the same way on Monday as it does on Friday. The result is a process that produces evidence you can trust, reduces noise across interviewers, and earns credibility with candidates who have options. Do that consistently and you will make fewer heroic hires and more durable ones, which is exactly what a robotics program needs when it graduates from demos to production.
