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Salary
≈ $132k – $294k per year (Estimated)
Location
In office (Boston)
Visa
H-1B filings in 12 months: 2
Employment
Full-Time

Confirmed on the employer's own hiring board on Oct 31, 2026. First seen by Alion on Apr 20, 2026. EKA Robotics scores C on the Alion truth index.

Overview
Company
Impact
Profile match
EKA Robotics is a robotics company building intelligence for the physical world, with robots that are fast, general and reliable. Its approach is grounded in physics and it hires robotics, machine learning, reinforcement learning and computer vision engineers. The company is headquartered in Boston, United States.

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

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