1X에서 휴머노이드 로봇 NEO를 위한 시뮬레이션 환경 및 인프라를 구축할 엔지니어를 찾습니다. Python/C++ 개발 역량과 로봇 시뮬레이터(MuJoCo, Isaac Sim 등) 활용 경험이 필수입니다. 물리적 정확성을 높여 Sim-to-Real 격차를 줄이고, AI 학습을 위한 합성 데이터 생산을 가속화하는 핵심 역할을 수행하게 됩니다.
We’re building humanoid robots that work in home - doing the chores, handling the tasks, and giving people their time back. Simple, but it’s not.
To do this right, we have to solve robotics, AI, manufacturing - at the same time, at scale, in a form factor that has to be safe enough to live with your family. If you’re inspired by this, you’ll thrive here. We’ve been at this since 2014 and we’re at the point where the hard problems are behind us and the hard work is in front of us.
NEO is our flagship - a home robot designed to move, learn, and operate in the real world alongside real people. We’re not demoing it - we’re shipping it. We’re excited to meet you, if this excites you.
If you’ve spent your career working on problems that matter and want to see them actually reach the world - this is that moment. We’re scaling, we’re hiring with intention, and we need people who want to build something that will genuinely change how humans spend their time - safely creating abundance for all.
The Simulation team builds the virtual environments and infrastructure that let 1X's AI team iterate on robot learning without being bottlenecked by real hardware. We construct physically realistic simulation worlds for NEO, scale synthetic data production, close the sim-to-real gap, and prototype new hardware virtually before it's manufactured. Our work is a force multiplier for every research and development team at 1X: the faster and more faithfully we can simulate NEO's world, the faster the whole company learns.
Build and maintain the simulation environments and real-time infrastructure that accelerate robot learning at 1X, reducing dependence on physical robot evaluations, scaling synthetic data production, and ensuring policies trained in simulation transfer reliably to real hardware. This is critical-path infrastructure: the AI team's iteration speed, the hardware team's ability to prototype, and the quality of training data all depend on simulation environments that are physically realistic, fast, correct, and well-maintained. You will own both the environments themselves and the systems that make them useful at scale.
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
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
$200,000 - $280,000 + Equity
Comprehensive medical, dental, and vision coverage
Generous paid time off, company holidays, and parental leave
401(k) plan with company match (100% on the first 3% of contributions, 50% on the next 2%)
Flexible Spending Accounts (FSA) and Health Savings Accounts (HSA) options
Commuter benefits (transit and parking)
Short-term and long-term disability, and life insurance
Employee Assistance Program (EAP) for mental health, financial, and personal support
Onsite snacks and catered lunches
1X is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender, gender identity or expression, sexual orientation, national origin, ancestry, citizenship, age, marital status, medical condition, genetic information, disability, military or veteran status, justice system impact, or any other characteristic protected under applicable federal, state, or local law.
