Staff Engineer - AI & Robotic Systems Test & Validation
Role details
Job location
Tech stack
Job description
Analog Devices' Edge AI team is developing intelligent sensing and autonomy solutions that combine AI-driven perception, classical robotics algorithms, and emerging policy-learning approaches for mobile and fixed-arm robotic systems. As these pipelines scale in capability and complexity, rigorous testing becomes essential to ensure robustness, reliability, and safe deployment in the physical world., We are seeking a Staff Engineer to lead the test strategy, validation frameworks, and system-level evaluation of our full AI and robotics algorithm stack - spanning SLAM/VIO, perception models, sensor fusion, manipulation policies, and robot-arm control.
Your work will establish the frameworks, datasets, and automated test pipelines that continuously validate both embodied AI perception and robotic behavior policies, ensuring that autonomous systems act predictably and safely across diverse real-world environments., * Lead the design and implementation of end-to-end test frameworks for AI-driven perception, SLAM, VIO, mapping, sensor fusion, and robotic manipulation policies.
- Architect scalable validation pipelines combining simulation, dataset replay, model-in-the-loop, policy-in-the-loop, and hardware-in-the-loop evaluation.
- Develop automated methods to test robot arm behaviors, including grasping, placement, pick-and-place sequences, collision avoidance, trajectory consistency, and policy robustness under uncertainty.
- Define performance and robustness metrics for: localization accuracy and driftloop closure stabilitymulti-sensor fusion reliabilitymanipulation success rates, compliance, and recovery behaviorreal-time policy stability and safety margins
- Build and curate datasets to evaluate perception and manipulation-including sensor-rich sequences, dynamic object interactions, and manipulation-specific datasets (RGB-D, force/torque, tactile, motion traces).
- Develop automated CI/CD test suites to detect regression across both AI models (vision, dynamics, policies) and classical algorithms (optimizers, planners, estimators).
- Collaborate closely with AI, SLAM, manipulation, motion planning, embedded, and hardware teams to ensure testability by design across the entire autonomy stack.
- Lead advancements in test methodologies, including simulation-to-real validation, domain randomisation, perturbation testing, and robustness evaluation for policy learning.
- Stay current with best practices in robotics simulation, embodied AI testing, safety validation, and performance benchmarking., Join us in shaping the future of dependable, embodied AI systems. You'll define how we test intelligence and robotic behavior - ensuring that perception, motion, and manipulation policies perform reliably in the real world. Your work enables robust autonomy across mobile robots, fixed-arm manipulators, and hybrid robotic systems operating in dynamic industrial environments. #LI-BF1 Job Req Type: ExperiencedRequired Travel: Yes, 10% of the timeShift Type: 1st Shift/Days
Requirements
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8+ years in robotics perception, manipulation, computer vision, AI model development, or system validation, including 3+ years building test or validation frameworks.
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M.S. or Ph.D. in Robotics, Computer Science, Electrical Engineering, or related field.
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Demonstrated expertise in: Testing or validating AI-based perception modelsEvaluation of robotic policies (learning-based or classical)Validation of SLAM, VIO, motion planning, or robotic arm controlHandling sensor data, time-series, and physical system dynamics
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Strong programming skills in Python and familiarity with C++ for integration into robotic systems.
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Experience with simulators and digital twins for both mobile and fixed-arm robots (Isaac Sim, Gazebo, Mujoco, PyBullet, Unreal Engine).
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Familiarity with ROS/ROS 2, robot kinematics libraries, and control/trajectory execution frameworks.
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Experience with CI/CD, MLOps, containerisation (Docker), and automated regression pipelines. Preferred Experience
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Testing robotic manipulation policies, including: imitation learning,reinforcement learning,hybrid model-based/model-free controllers,or foundation-model-based manipulation pipelines.
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Robustness validation of policies under noise, domain shift, and rare event conditions.
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Contributions to open-source robotics testing tools (benchmarking suites, simulators, manipulation datasets).
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Experience with uncertainty estimation, safety monitoring, or formal methods for robotic behavior validation.
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Knowledge of sensor modelling, synthetic data pipelines, and automated simulation scenario generation for manipulation and perception tasks.
