Persons in these roles are expected to work from our offices in Seattle. On-site requirements vary based on position and team. If you have questions about on-site work arrangements for this role, please ask your recruiter.

Our base salary range is $128,880 - $193,320, and in addition we have generous bonus plans to provide a competitive compensation package.

Who You Are

You are a simulation engineer who makes physics behave. You have written contact and constraint solvers yourself. When a stack jitters, a grasp slips, an object tunnels through a finger or a gripper linkage explodes, you can tell whether the cause is the timestep, constraint softness, the friction model, the joint formulation or the asset itself, and you fix the root cause rather than adding damping until the problem goes away.

You also know that a stable simulator is not the same as a correct one. You treat logged data from real robots as ground truth. You replay the same motion in simulation, look at the residuals, and close the gap with physics you can explain rather than with magic numbers. You want simulation that a robot foundation model can train on at scale, that never blows up at step ten million, and that behaves like the real world when the policy gets there.

Who We Are

Ai2’s Robotics team is led by Dieter Fox. We develop scalable simulation domains to study how robot foundation models scale and generalize, and we build robots that are broadly competent in the real world.

Simulation is central to that mission. We train and evaluate policies across MuJoCo, Isaac Sim/Isaac Lab on Newton, and other GPU simulators, and we deploy them on real bimanual and single-arm manipulators. Whether simulated data is worth anything depends on two things: the simulator must be stable enough to run at scale, and close enough to reality that what is learned there transfers. This role owns both.

Our focus areas include manipulation, large-scale training in simulated environments, sim-to-real transfer, robot foundation models and skill learning. We publish at CoRL, ICRA and RSS, as well as CVPR, ICCV, ECCV, NeurIPS, ICLR and ICML, and we release our models, data and tools openly.

We work closely with other teams across Ai2 and with the University of Washington, only 1.5 miles away.

Your Next Challenge

The needs below are competencies, not a checklist. If you match many of them but not all, we encourage you to apply.

Stability: Make the Simulator Robust at Scale

Design, implement and improve contact, friction and constraint solvers: complementarity and soft-constraint formulations, pyramidal versus elliptic friction cones, warm-starting, iteration counts and tolerances.
Find and remove the failure modes that break large-scale training:
Jitter in resting contact.
Interpenetration and tunnelling.
Energy gain.
Constraint drift in closed kinematic chains.
Instability under stiff joint drives.
Non-determinism across runs and GPUs.
Make articulated robots and closed loops stable and accurate across MuJoCo, Newton and PhysX: parallel grippers, mimic and coupled joints, tendons. Contribute fixes upstream to those open-source engines.
Choose integrators, timesteps and substeps with evidence. Show how results change with dt, find where a scene stops converging, and quantify the trade-off between accuracy and throughput for GPU-batched training across thousands of environments.
Build stress and regression tests for physics: long-horizon rollouts, randomized scenes, extreme parameters and contact-rich edge cases, with failures caught before they reach a training run.
Make assets simulation-ready: collision geometry, inertial properties, joint limits and contact settings that are stable by construction, consistent across URDF, MJCF and OpenUSD.

Fidelity: Bring Simulated Behaviour Closer to Real

Fit simulation models to logged hardware data: joint friction (Coulomb, viscous, Stribeck), armature and reflected inertia, payload mass and CoM, actuator drive gains, and latency. Identify what the simulator is missing rather than absorbing it into other parameters.