Machine Learning / Reinforcement Learning Engineer

Responsibilities

• Algorithm Development: Research and implement reinforcement learning and supervised learning algorithms for robotic manipulation.
• Simulation: Design simulation models and domain randomization strategies; collaborate with the robotics team to ensure alignment with physical systems.
• Performance Optimization: Design experiments to evaluate and optimize model architectures for sample complexity and policy performance with real-time execution constraints.
• Data & Pipeline Engineering: Develop scalable data management pipelines for real and synthetic data; evaluate and select algorithms that maximize data efficiency and overall policy performance.
• On-Robot Evaluation: Deploy, evaluate, and debug policies on physical hardware; identify bottlenecks and implement improvements in collaboration with the robotics team.

Qualifications

• Education: BS, MS, or PhD in Computer Science, Robotics, or a related field.
• Core Expertise: Deep theoretical and practical knowledge of reinforcement learning and supervised learning algorithms.
• Robotics Toolkit: Experience with physics engines (e.g., Isaac Sim, MuJoCo, PyBullet) and robotics middleware (ROS/ROS2).
• Architectural Depth: A deep understanding of modern architectures, including Transformers, CNNs, and Foundation Models.
• Technical Proficiency: Expert-level Python skills and proficiency in deep learning frameworks such as PyTorch or JAX.
• Engineering Rigor: A strong commitment to clean code, version control, and reproducible experimental workflows.
• Track Record: A history of publications in top-tier robotics or machine learning conferences, or a portfolio of projects providing strong practical evidence of expertise in the field.