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# Software Engineer - Simulation

- **Company:** 1X, LLC
- **Location:** San Carlos, CA, United States
- **Experience:** Experienced
- **Salary:** $200,000.0 - $280,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, C++ (Programming Language), Python (Programming Language), OpenGL, Software Engineering, Information Technology
- **Published:** June 18, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=76996a843e57ed7f

## About the Role

Do you have experience in Software engineering?, * Physics simulation depth understanding what makes a simulator physically accurate and computationally tractable; knows how to tune contact dynamics, articulated body models, and rendering fidelity for robot learning applications

 * Sim-to-real instincts having practical experience narrowing the gap between simulated and real behavior; knows which differences matter for policy transfer and which can be addressed through domain randomization
 * Performance-oriented engineer optimizing physics and rendering pipelines to maximize simulation throughput; thinks carefully about the tradeoff between fidelity and speed for different use cases
 * Rigorous infrastructure builder writing tested, maintainable simulation code that other teams can depend on; treats correctness and reliability of the simulation stack as a first-class engineering concern, * 4+ years of experience programming in Python, C++, or similar languages, with experience building environments or benchmarks using robotics simulators (MuJoCo, PyBullet, Isaac Sim, or equivalent)
 * Experience improving the performance of physics simulators or OpenGL rendering pipelines
 * Strong testing practices for simulation stacks used in robot learning-comfortable writing tests that verify physical correctness and catch regressions
 * Advanced degree (MS or PhD) in Computer Science or a related field, * Knowledge of extrinsic and intrinsic calibration algorithms for robotics, and experience using calibration to improve sim-to-real fidelity
 * Experience with domain randomization, procedural environment generation, or other techniques for scaling diverse simulation data production
 * Familiarity with differentiable simulation or GPU-accelerated physics (Warp, JAX-based simulators) for high-throughput parallel training
 * Background in legged locomotion, dexterous manipulation, or contact-rich robotics where simulation accuracy is most critical

## Description

* Deliver diverse, physically realistic simulation environments for NEO that enable the AI team to develop and evaluate new policies without requiring real robot time for every iteration
 * Measurably narrow the sim-to-real gap through domain randomization, calibration, and environment fidelity improvements, such that policies trained in simulation transfer reliably to deployed hardware
 * Scale synthetic data production to meet the AI team's training needs, with infrastructure that generates diverse, high-quality simulation data efficiently and reproduces environments reliably
 * Enable the hardware team to prototype and virtually test new robot hardware in simulation before manufacturing, reducing design iteration cycles and surfacing issues earlier


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