Machine Learning / Reinforcement Learning Engineer

Roboticsconnected; Boston (MA); On-site; USD 140,000 - 210,000; Full time

Job summary

Eka Robotics is hiring for a hands-on researcher to advance reinforcement learning and supervised learning for robotic manipulation. You will work on simulation, real-world deployment, and data pipelines, collaborating across teams to push performance under real-time constraints.

Applicants should have strong Python expertise, experience with PyTorch or JAX, and a track record of impactful work in robotics or ML, including publications or a solid project portfolio.

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 (Isaac Sim, MuJoCo, PyBullet) and robotics middleware (ROS/ROS2).
• Architectural Depth: Understanding of Transformers, CNNs, Foundation Models.
• Technical Proficiency: Expert-level Python; proficient in PyTorch or JAX.
• Engineering Rigor: Clean code, version control, reproducible experiments.
• Track Record: Publications in top robotics or ML conferences or strong project portfolio.

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.

Skills

Reinforcement learning

Supervised learning

Transformers

CNNs

Foundation Models

Python

PyTorch

JAX

Clean code

Publications

Education

BS/MS/PhD in CS/Robotics

Tools

Isaac Sim

MuJoCo

PyBullet

ROS/ROS2

Job description

Eka Robotics

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.