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.