Evaluate Robotics Software Engineer candidates across 4 weighted areas: technical proficiency, systems and trade-offs, evidence and rigour, and collaboration and communication. Technical proficiency leads at 35%, so probe depth in C++ and Python on ROS or ROS 2: nodes, tf2 frames, real-time control loops, SLAM stacks, MoveIt or custom motion. Use the rubric to compare role-specific evidence consistently.
For technical proficiency, look for evidence the candidate names specific stacks (Nav2, MoveIt, Cartographer), explains tf tree design and controller timing, and discusses determinism on real hardware. For systems and trade-offs, look for evidence the candidate weighs compute budget, sensor cost, and safety margins explicitly, with a reasoned choice they later validated or reversed on data.
Apply the written 1–5 anchors to every answer, record the evidence behind each rating, and use the factor weights to reach a consistent overall assessment.
Complete evaluation framework
What to assess and how to score it
Review the evidence signals before interviewing. Then use the anchored descriptions—not instinct alone—to choose the score that best matches each answer.
01
Evaluation factor
Technical proficiency
35% weight
Probe depth in C++ and Python on ROS or ROS 2: nodes, tf2 frames, real-time control loops, SLAM stacks, MoveIt or custom motion planners, and driver integration over CAN or EtherCAT.
Evidence to listen for
Command of the languages, frameworks, and data tools the role actually uses
Understands correctness, performance, and failure modes, not just syntax
Has opinions on testing and can justify them
Reads and reasons about code they did not write
Five-point scoring guide
1
Poor
Cannot work independently; fundamentals are missing.
2
Needs Improvement
Weak fundamentals; output needs heavy review.
3
Satisfactory
Competent for the role; needs guidance on complex or unfamiliar work.
4
Very Good
Strong practitioner; handles hard problems with little guidance.
5
Excellent
Names specific stacks (Nav2, MoveIt, Cartographer), explains tf tree design and controller timing, and discusses determinism on real hardware.
02
Evaluation factor
Systems and trade-offs
25% weight
Ask how they traded perception latency against accuracy, chose sensor fusion (EKF versus factor graph), or partitioned compute between onboard microcontrollers, Jetson boards, and fleet software.
Evidence to listen for
Reasons about scale, latency, cost, and failure before writing code
Names the trade-off they chose and what they gave up
Understands the data lifecycle end to end
Anticipates what breaks at ten times the volume
Five-point scoring guide
1
Poor
No thinking beyond the immediate task; no awareness of scale or failure.
2
Needs Improvement
Limited architectural awareness; struggles with design decisions.
3
Satisfactory
Works within an existing design; makes sound local decisions.
4
Very Good
Designs for scale and maintainability; articulates trade-offs clearly.
5
Excellent
Weighs compute budget, sensor cost, and safety margins explicitly, with a reasoned choice they later validated or reversed on data.
03
Evaluation factor
Evidence and rigour
25% weight
Check how they validate: rosbag replay, Gazebo or Isaac Sim regression suites, hardware-in-the-loop rigs, unit tests on kinematics, and metrics like localisation drift or pick success rate.
Evidence to listen for
Validates results rather than trusting output
Knows how their work is measured and what a bad result looks like
Can describe a time their own analysis or model was wrong and how they caught it
Careful about data quality, leakage, and silent failure
Five-point scoring guide
1
Poor
Ships unvalidated work; no notion of how correctness is checked.
2
Needs Improvement
Validates superficially; misses obvious quality or leakage issues.
3
Satisfactory
Reasonable checks in place; rigour drops under time pressure.
4
Very Good
Validates thoroughly; can name a real error they caught in their own work.
5
Excellent
Cites measured before and after numbers, replayed logs to reproduce field failures, and trusts simulation only where correlated with hardware.
04
Evaluation factor
Collaboration and communication
15% weight
Look for work alongside mechanical, electrical, and controls engineers plus field technicians: interface contracts, bring-up of new hardware revisions, and triaging failures reported from deployed robots.
Evidence to listen for
Explains technical work to non-technical stakeholders
Gives and takes code or peer review constructively
Documents enough that the work survives their absence
Aligns with team process rather than working around it
Five-point scoring guide
1
Poor
Cannot work in a team; resistant to feedback.
2
Needs Improvement
Communication issues create rework; lone-wolf tendencies.
3
Satisfactory
Adequate team member; documentation and review participation are light.
4
Very Good
Communicates well; reliable reviewer and collaborator.
5
Excellent
Describes joint bring-up sessions, writes interface docs others use, and translates field failure reports into precise, reproducible engineering tickets.
Evidence-led prompts
Interview questions for a Robotics Software Engineer
Use these prompts to surface evidence for the weighted factors above and compare candidates against the same role-specific criteria.
01
Have you worked with specific types of robots such as industrial, mobile or service robots?
02
Can you provide an example of a project that involved both hardware and software integration?
03
Can you discuss a time when you had to optimise the performance of a robotic system?
04
How do you handle real-time constraints when developing robotics software?
05
What approaches do you use for ensuring the reliability and safety of your software?
