What we’re doing isn’t easy, but nothing worth doing ever is. We envision a future powered by robots that work seamlessly with human teams. We build artificial intelligence that enables service robots to collaborate with people and adapt to dynamic human environments. Join our mission-driven, venture-backed team as we build out current and future generations of humanoid robots. As a Sr ML Engineer, Robotic Manipulation , you’ll drive the development of advanced learning-based manipulation systems. You’ll focus on multimodal perception, grasping, dexterous control, and embodied reasoning—ensuring our robots can safely and robustly interact with the world around them. This is a hands-on role: you’ll design models, implement algorithms, and bring manipulation skills from research into real-world deployment. Responsibilities Design and implement ML algorithms for robotic manipulation, including grasp planning, dexterous manipulation, and tool use. Develop and train large multimodal ML models (vision, tactile, language, action) to enable robust manipulation behaviors. Build pipelines for data collection, labeling, and augmentation to support manipulation learning. Leverage simulation environments (Isaac Gym, MuJoCo, Omniverse, etc.) for training, evaluation, and transfer to real robots. Optimize models for onboard, real-time performance on robotic hardware. Collaborate with perception, navigation, and platform teams to integrate manipulation skills into the full robotics stack. Analyze robot performance, develop benchmarks, and iterate based on real-world deployments. Contribute to code reviews, documentation, and best practices for ML/robotics development. Stay current with state-of-the-art manipulation and embodied AI research, bringing promising ideas into production. Skills and Experience Master’s or PhD in Computer Science, Robotics, Machine Learning, or related field. 5+ years of experience in applied machine learning, computer vision, or robotics. Strong background in deep learning frameworks (PyTorch, TensorFlow, JAX). Hands-on experience building and deploying ML models robotic manipulation for grasping, manipulation, or dexterous robotics. Expertise with large multimodal ML models (vision-language-action, tactile sensing). Experience with simulation for manipulation (MuJoCo, PyBullet, Isaac, or equivalent). Familiarity with SLAM, mapping, and navigation pipelines. Solid software engineering skills in Python and C++ for ML system integration. Proven ability to take ML models from research prototype to production deployment. Strong debugging skills for diagnosing ML performance gaps in fielded systems.