Research Engineer – Simulation
ekarobotics
Boston, MA · Posted April 20, 2026
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November 2026
- #1Best cities for life sciences and biotech jobs793 openings · of 41
- #1Cities with the most jobs that do not need a four-year degree in New England390 jobs · of 14
- #1Best cities for AI-skill jobs in New England21% · of 28
- #1Best cities for software and engineering jobs in New England910 openings · of 24
- #1Best all-round cities for mid-career professionals in the Northeast71 / 100 · of 26
- #1Best all-round cities for mid-career professionals in New England71 / 100 · of 11
Rankings about engineering jobs
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- Best cities for engineering pay after cost of living #1 San Mateo ($216k)
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About the job
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
- Evaluate and extend physics engines (e.g. MuJoCo, Newton)
- Scale and optimize GPU-based physics simulators for massive training workloads
- Improve the realism of contact-rich manipulation environments
- Create tools and automated workflows to validate, tune, and optimize physical and visual assets
MINIMUM QUALIFICATIONS
- Bachelor’s degree or higher in Computer Science, Robotics, Engineering, or a related field
- Strong C++ and Python programming skills
- Hands-on experience with physics engines
- Proven ability to build and scale high-performance, physics-based simulation environments
PREFERRED QUALIFICATIONS
- Strong mathematical & physics background – particularly expertise in rigid/soft-body dynamics, contact mechanics, FEA, and numerical stability
- Experience across the entire simulation stack, from CAD import and mesh optimization to real-time rendering
- Experience with reinforcement learning, policy training, differentiable physics, or neural simulation models
- Background in contact-rich manipulation or legged locomotion
- Strong knowledge of GPU acceleration and robotics model formats (URDF, SDF, USD)
- Proven success deploying simulation-trained policies onto real-world hardware