Machine Learning / Reinforcement Learning Engineer
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Before you apply
SalaryNo pay range posted
LocationBoston Area
Work modeon-site
Typefull-time
DepartmentEngineering
Company size51–200 people
PostedMar 8, 2025 · 1y ago
Verified live16h ago
May no longer be actively hiring
Posted by the company on Mar 8, 202555% of this company's open roles have been listed for 90+ daysNo pay range listed, though Massachusetts requires one
At a glanceSummarised by Seekless from the posting.
BS, MS, or PhD in Computer Science, Robotics, or a related field
Deep theoretical and practical knowledge of reinforcement learning and supervised learning algorithms
Experience with physics engines (e.g., Isaac Sim, MuJoCo, PyBullet) and robotics middleware (ROS/ROS2)
A deep understanding of modern architectures, including Transformers, CNNs, and Foundation Models
Expert-level Python skills and proficiency in deep learning frameworks such as PyTorch or JAX
A strong commitment to clean code, version control, and reproducible experimental workflows
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
Skills
Python
PyTorch
JAX
Isaac Sim
MuJoCo
PyBullet
ROS
ROS2
Transformers
CNNs
Foundation Models
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
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Algorithm Development: Research and implement reinforcement learning and supervised learning algorithms for robotic manipulation.
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Simulation: Design simulation models and domain randomization strategies; collaborate with the robotics team to ensure alignment with physical systems.
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Performance Optimization: Design experiments to evaluate and optimize model architectures for sample complexity and policy performance with real-time execution constraints.
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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.
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On-Robot Evaluation: Deploy, evaluate, and debug policies on physical hardware; identify bottlenecks and implement improvements in collaboration with the robotics team.
Qualifications
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Education: BS, MS, or PhD in Computer Science, Robotics, or a related field.
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Core Expertise: Deep theoretical and practical knowledge of reinforcement learning and supervised learning algorithms.
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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.
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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.