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Research Engineer – Simulation

EkaroboticsBoston Area🇺🇸United StatesPosted Apr 20, 2026

Why This Role Stands Out

You'll have the opportunity to push the boundaries of robotics by enhancing physics engines and scaling high-performance simulation environments, making this an ideal role for someone passionate about building intelligence for the physical world. If you possess strong C++ and Python skills and hands-on experience with physics engines, you'll thrive in this position at a company defining the future of robotics. Apply today to join a team of pioneers and shape the future of robotics!

Quick Overview

Seniority
Mid Senior
Employment type
Full Time
Location
Boston Area, United States
Posted
4 months ago
RoboticsCADFEA

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

  • 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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