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1X

Simulation Engineer - Physics Modeling

1X is hiring a Simulation Engineer - Physics Modeling. It pays $200k-$300k a year and Level rates it ; you can apply on 1X.

Level's read on this job

  • Level rates this role Works on AI ●●●○: the daily work is on AI products, without building the model.
  • The posting mentions JAX, Python and C among its tools and skills.
  • Level classes it as a mid-level role.
  • 1X has 101 other open roles on Level.

AI in this role

Build and validate physically accurate simulation environments and physics models to accelerate robotic learning and sim-to-real transfer.

JAXPythonCMujocoRos
physics-modelingsimulationroboticskinematics

About 1X

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.

About the Team

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.

Your Charter

Close the gap between how NEO behaves in simulation and how it behaves in the world. You will make the simulator physically accurate enough that results earned in simulation hold up on hardware, building and validating the dynamics, material, and sensor models that decide whether a simulated result means anything. Every team that trains or evaluates against simulation is betting on the fidelity you deliver.

Key Outcomes

  • Physical Accuracy: Improve contact dynamics, articulated body models, and actuator and transmission fidelity, driven by system identification data from real hardware and kept current as hardware changes.

  • Compliant and Deformable Bodies: Model soft contact and structural compliance where rigid-body assumptions break down, so grasping and contact-rich behavior transfers instead of only working in simulation.

  • Sensing Realism: Model the sensing path as it actually behaves - noise, bias, dropout, latency, and sample timing - so a policy sees in simulation what it will see on the robot.

  • Measuring the Gap: Establish the benchmarks and real-robot comparisons that quantify sim-to-real error, so fidelity work is driven by evidence rather than intuition.

  • Randomize or Model: Decide which discrepancies to model faithfully and which to randomize over, with ranges grounded in the spread measured across the real fleet.

  • Determinism and Reproducibility: Make runs reproducible given the same inputs, so a regression is a real regression and any failure can be replayed and debugged.

  • Throughput and Environments: Optimize physics and rendering, and deliver the diverse environments and synthetic data pipelines that feed policy training and evaluation.

Key Competencies

  • Physics Simulation Depth: Knows what makes a simulator physically accurate and computationally tractable, and how to tune contact dynamics and articulated body models for robot learning.

  • Sim-to-Real Judgment: Knows which differences between simulation and reality actually matter for policy transfer, and which can be absorbed by randomization.

  • Grounded in Real Data: Validates fidelity against measurements from real robots rather than trusting a simulation that looks right.

  • Fleet Thinking: Treats unit-to-unit variation across real robots as something to measure and model, not average away, so simulation reflects the fleet rather than one nominal unit.

  • Performance Engineering: Optimizes physics and rendering pipelines for throughput, and reasons explicitly about the tradeoff between fidelity and speed.

  • Rigorous Infrastructure: Writes tested, maintainable simulation code other teams depend on, treating determinism and correctness of the stack as first-class concerns.

  • Technical Direction: Sets the approach for a problem area, defends tradeoffs across fidelity, throughput, and effort, and partners with the teams that depend on simulation.

Minimum Requirements

  • Strong programming in Python, C++, or similar languages, with experience building environments or benchmarks using robotics simulators (MuJoCo, PyBullet, Isaac Sim, or equivalent)

  • Demonstrated experience improving simulation fidelity and closing the sim-to-real gap for behavior that had to work on physical hardware

  • Experience modeling sensors and their failure modes in simulation, including noise, bias, latency, and dropout

  • Experience improving the performance of physics simulators or rendering pipelines

  • Strong testing practices for simulation stacks used in robot learning, including tests that verify physical correctness and catch regressions

  • 5+ years of relevant industry experience, or an advanced degree plus equivalent hands-on experience

Preferred Skills

  • Experience with system identification on real hardware, and with turning measured parameters into simulation models

  • Experience with procedural environment generation or other techniques for scaling diverse simulation data production

  • Familiarity with differentiable simulation, learned or neural simulators, or GPU-accelerated physics such as Warp or JAX-based frameworks

  • Background in legged locomotion, dexterous manipulation, or contact-rich and deformable-body simulation

Compensation

  • Salary Range: $200,000 - $300,000 + Equity

Benefits

  • 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

Equal Opportunity Employer

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.

How we rate this

Simulation Engineer - Physics Modeling at 1X rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Physics ModelingSimulationRoboticsKinematicsJAXPythonCMujoco

Questions you could be asked

  1. Tell me about a project where physics modeling was part of your work. What did you do?
  2. Tell me about a project where simulation was part of your work. What did you do?
  3. Tell me about a project where robotics was part of your work. What did you do?
  4. Tell me about a project where kinematics was part of your work. What did you do?
  5. Walk me through how you've used JAX in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Physics Modeling, Simulation, Robotics, Kinematics, and JAX. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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