Machine Learning Engineer - Robot Manipulation
Designs and deploys reinforcement and imitation learning algorithms for robotic manipulation tasks in dynamic environments. Requires MS/PhD, deep RL/IL expertise, PyTorch proficiency, and real-world ML deployment experience in a fast-paced startup.
About the job
Responsibilities
- Design and implement machine learning algorithms, focusing on reinforcement learning (RL) and imitation learning (IL), for robotic manipulators in dynamic environments.
- Translate high-level objectives into ML problems and deploy robust, scalable models to real-world robotic systems.
- Integrate ML solutions into robotics workflows, ensuring performance in simulated and real-world settings.
- Drive innovation by applying latest ML research to robotic manipulation.
- Own critical ML projects from conception to deployment.
- Collaborate across disciplines and mentor junior engineers.
Requirements
Must-have:
- MS or PhD in machine learning, computer science, robotics, or related field.
- Strong experience training and deploying ML models for real-world applications.
- Deep understanding of RL and IL in robotics.
- Proficiency in Python, PyTorch.
- Experience with data collection, preprocessing, and management for ML training.
- Self-starter with problem identification, prioritization, and execution skills.
- Enthusiasm for fast-paced startup environment.
Nice-to-have:
- Familiarity with Gazebo, MuJoCo, sim-to-real transfer.
- Designing reward functions for manipulation tasks.
- Models for noisy, incomplete, or sparse data.
- Deployment to edge devices for real-time inference.
- Accelerating training with GPU, TPU, or accelerators.
- Stable Baselines, RLlib.
- Robotics principles: kinematics, dynamics, control.
- Publications in ML/robotics/RL.
Skills
Reinforcement Learning, Imitation Learning, PyTorch, Python, Gazebo, Mujoco, Stable Baselines, Rllib, Sim-To-Real Transfer, GPU, Tpu
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