Machine Learning / Reinforcement Learning Engineer

Eka Robotics; Massachusetts; On-site; USD 140,000 - 210,000; Full time

Job summary

Eka Robotics is on a mission to build intelligence for the physical world — robots that are fast, general, and reliable. We combine physics-based insights with learning to push robotics research from theory to real-world deployment.

We seek hands-on researchers who can design RL and supervised learning solutions, build simulation pipelines with domain randomization, evaluate architectures for sample efficiency and real-time performance, and validate policies on real hardware, collaborating

Qualifications

• Advanced degree in CS/Robotics or related field.

Responsibilities

• Research and implement reinforcement learning and supervised learning algorithms for robotic manipulation.
• 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.

Skills

Reinforcement learning

Supervised learning

Python

PyTorch

JAX

Transformers

CNNs

Foundation Models

Robotics

Experimentation

Education

BS/MS/PhD in Computer Science, Robotics, or related field

Tools

Isaac Sim

MuJoCo

PyBullet

ROS/ROS2

Git

Job description

Eka Robotics is on a mission to build intelligence for the physical world - robots that are fast, general, and reliable. Our approach, grounded in physics, unlocks superhuman capabilities. We are defining the frontier of robotics research and deployment.

Our team consists of pioneers in robotics and machine learning. We are now hiring to scale our R&D effort. We are looking for hands-on individuals who are excited to help shape the future of robotics.

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.