Machine Learning / Reinforcement Learning EngineerEka • Cambridge, Massachusetts, United States
Machine Learning / Reinforcement Learning Engineer
Eka
- Cambridge, Massachusetts, United States
- Cambridge, Massachusetts, United States
À propos
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.
Compétences linguistiques
- English
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