Machine Learning / Reinforcement Learning Engineer
Confirmed on the employer's own hiring board on Oct 29, 2026. First seen by Alion on Mar 8, 2025. EKA Robotics scores C on the Alion truth index.
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.