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Certis Integrated Services • Singapore, Singapore
Role & seniority: Robotics QA Engineer; mid-to-senior level role focused on end-to-end quality for real-world robotics systems.
Stack/tools: Python and/or C++ for test automation; Linux, networking basics; robotics/AI elements; ROS/ROS2 (preferred); simulation tools (Gazebo, Isaac Sim, MuJoCo); hardware-in-the-loop; CI; log capture and telemetry tooling.
Define and own test strategy across simulation, lab, and field; establish release gates and acceptance criteria for autonomy, manipulation, robotics behavior, and AI updates.
Robotic validation and test engineering: design/test scenarios, metrics, datasets; ensure safety behaviors; create site-acceptance checklists and regression suites.
Automation and tooling: build automated test harnesses, CI-integrated regression tests, reproducible test environments; develop tooling for logs, event replay, labeling, and metrics dashboards; manage defect triage and root-cause analysis.
3+ years QA/test engineering for autonomous, embedded, or AI-heavy products.
Strong understanding of autonomous system failure modes (sensors, localization, navigation, motion control, safety).
Hands-on test automation in Python and/or C++; experience with Linux; basic networking; hardware integration troubleshooting.
Ability to design rigorous test plans and communicate precisely.
ROS/ROS2, Gazebo/Isaac Sim/MuJoCo; hardware-in-the-loop setups.
Experie
Role Summary We are hiring a Robotics QA Engineer to establish and run end-to-end quality practices for real-world robotics systems deployed in public and shared spaces. You will validate robotic autonomy (navigation/localization), manipulation behaviours, human robot interaction, and AI perception and agent components - ensuring releases are safe, reliable, and measurable across hardware and environments. This role requires both breadth (robotics + AI + systems) and depth (building repeatable test methods, automation, and rigorous acceptance criteria). You will work closely with robotics engineers, AI engineers, and deployment teams.
Responsibilities Test strategy and quality ownership Define a comprehensive test strategy across simulation, lab, and field environments. Establish release gates and acceptance criteria for autonomy, manipulation, robotics behaviour, and AI model updates.
Own defect triage workflow: reproduce issues, isolate root causes with logs/telemetry, and drive closure. Robotics validation and test engineering
Navigation, Manipulation，Human Robot Interaction: Design and execute test scenarios, and task success metrics; build test datasets; validate safety behaviours; establish repeatable site-acceptance checklists and regression suites.
AI/ML models and Agents: Define evaluation protocols; validate agent/planner behaviour; support model release governance. Automation and tooling Build automated test harnesses, CI-integrated regression tests, and reproducible test environments. Develop tooling for log capture, event replay, labelling for failure analysis, and metrics dashboards.
Required qualifications 3+ years in QA/test engineering for autonomous systems, embedded systems, or AI-heavy products.
Strong understanding of autonomous systems and failure modes: sensors, localization, navigation, motion control, and safety behavior. Hands-on experience building test automation in Python and/or C++. Proven ability to design rigorous test plans to debug cross-disciplinary issues. Experience with Linux environments, networking basics, and hardware integration troubleshooting.
Strong communication skills: can write precise test reports and collaborate effectively with engineers.
Preferred qualifications Experience with ROS/ROS2, simulation tools (e.g., Gazebo, Isaac Sim, Mujoco), hardware-in-the-loop test setups. Experience with voice/NLP or multi-modal interaction systems (ASR/TTS latency, intent routing, noisy environment testing). Experience with model evaluation pipelines (dataset splits, regression testing). Familiarity with safety or compliance practices for robots operating around people (risk analysis, operational constraints).
Experience building debug pipelines: telemetry, log aggregation, incident replay.