ML Engineer – Robotics

About the Role We are a Series A AI/ML data and services company building highly accurate AI through high-quality training data, robust reinforcement learning environments, and intelligent agents for frontier labs and enterprises. Our work spans the full AI lifecycle — from data labeling and post-training to evaluation and deployment — with a strong focus on physical intelligence and embodied AI. As an ML Engineer – Robotics, you will design, train, and deploy intelligent models that power autonomous systems at the intersection of machine learning, control systems, and real-world robotics. You will build perception, planning, and decision-making pipelines that make machines truly adaptive, collaborating with world-class AI teams to solve hard, interdisciplinary problems that combine data-driven learning with real-world constraints. What You'll Do Develop and optimize ML models for perception, motion planning, and control. Build computer vision and sensor fusion systems using camera, LiDAR, and IMU data. Integrate learning-based models with robotics software stacks (ROS/ROS2). Design pipelines for data collection, simulation, and reinforcement learning. Collaborate with robotics and hardware engineers to deploy models in live environments. Continuously evaluate model performance and robustness across diverse real-world scenarios. What We're Looking For Required: 3–8 years of professional experience in Machine Learning, Robotics, or Computer Vision. Proficiency in Python and C++ for robotics and ML development. Hands-on experience with PyTorch and/or TensorFlow for model development. Proficiency with ROS or ROS2 and integrating ML models into robotics software stacks. Experience with robotics simulation and benchmarking tools (Gazebo, Isaac Sim, CARLA, MuJoCo, PyBullet). Experience designing and deploying perception, motion planning, and control pipelines for autonomous systems. Experience with sensor fusion using camera, LiDAR, and IMU data. Experience with data collection pipelines, simulation environments, and reinforcement learning workflows. Strong ability to evaluate model performance and robustness across diverse deployments. Nice to Have: Familiarity with localization, SLAM, or adaptive control techniques. Experience with imitation learning or model-based reinforcement learning. Background deploying ML models in real-time or embedded environments. Eligibility: Candidates must be eligible to work in the United States without company visa sponsorship. No visa sponsorship is available for this role. Compensation & Benefits Salary range: $220,000 – $300,000 USD annually, depending on experience. Location This is a fully on-site role based in Mountain View, CA. Local candidates or those willing to relocate are preferred; remote work arrangements are not available for this position.