We are looking for a Machine Learning Engineer with experience building and deploying perception and sensor-based ML systems in robotics or autonomous platforms. The role involves working across the full lifecycle of data, models, and deployment—from raw sensor streams to production-ready perception modules.
This is a great fit for someone who has worked with LiDAR, cameras, radar, IMUs, or multimodal pipelines, and who enjoys taking ML systems from prototype to field-tested reality.
Key Responsibilities
Perception & Sensor Fusion
Develop ML pipelines for multimodal sensor data (LiDAR, cameras, radar, IMU, etc.).
Implement or support sensor fusion approaches (classical or ML-based).
Build models and processing steps for perception tasks such as detection, tracking, mapping, or scene understanding.
Cross-Functional Collaboration
Work closely with robotics engineers, software teams, and simulation teams to ensure seamless integration of ML perception modules.
Contribute to design discussions involving sensing hardware, data capture strategies, and operational requirements.
Model Development & Deployment
Train, evaluate, and optimize ML models for robotics perception under real-world constraints.
Deploy ML components to diverse environments (edge devices, robotics stacks, cloud backends).
Collaborate on performance tuning, latency improvements, and reliability enhancements.
If you have
Experience working with robotics or autonomous systems.
Hands-on work with LiDAR, cameras, radar, or IMU pipelines.
Strong Python and ML fundamentals, with experience in at least one major framework (PyTorch, TensorFlow).
Experience designing or maintaining sensor-based ML systems, including data preparation and evaluation.
Understanding of model deployment in real systems (edge devices, robotics stacks, embedded platforms, or cloud).
It's a plus
Experience with sensor fusion frameworks, classical or ML-based.
Familiarity with robotics middleware (ROS/ROS2), mapping, SLAM, or navigation stacks.
Exposure to simulation tools (Isaac Sim, Gazebo, Unity, Webots).
Experience improving performance of models under real-time constraints.
Background working with safety, reliability, or high-availability systems.
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