Research Engineer/Scientist, Simulation

Dyna Robotics makes general-purpose robots powered by a proprietary embodied AI foundation model that generalizes and self-improves across varied environments with commercial-grade performance. Dyna's robots have been deployed at customers across multiple industries. Its frontier model has the top generalization and performance in the industry. Dyna Robotics has raised over $140M, backed by top investors including CRV, First Round Capital, Robostrategy, Salesforce Ventures, NVentures, Amazon, Samsung Next, and LG Technology Ventures. Our team brings together engineers and researchers from Google, Meta, Apple, Amazon, Cruise, Aurora, NVIDIA, along with academic roots at Stanford, Berkeley, MIT, UPenn, and beyond. We're positioned to redefine the landscape of robotic automation. POSITION OVERVIEW As a Research Engineer/Scientist focused on Simulation, you will build the simulated worlds that power Dyna's loco-manipulation policies. Our robot is a wheeled mobile manipulator. Mobility and manipulation are tightly coupled, not two separate problems, so simulating one without the other misses what actually matters for our policies. You'll own the pipeline end-to-end: from real-to-sim reconstruction of the facility-scale environments our robots navigate, through procedural scene generation, to sim-based training data and evaluation for policies that jointly control base motion and arm manipulation. WHAT YOU'LL DO Real-to-Sim Reconstruction: Build a pipeline that reconstructs real, facility-scale environments into simulation (3D reconstruction, photorealistic re-rendering). That means full rooms and aisles our wheeled base navigates, not just tabletop scenes. Facility-Scale Scene Generation: Procedurally generate navigable environments (layouts, obstacles, object placement) that stress-test loco-manipulation policies across the range of spaces our robots actually operate in. Loco-Manipulation Sim-for-Data-Gen: Generate synthetic training data for policies that jointly reason about base positioning and arm manipulation (approach angles, reachability, obstacle-aware repositioning), and feed real-world failure modes back into new sim scenarios. Loco-Manipulation Evaluation: Build simulation benchmarks that test the joint base+arm policy (VLA / imitation learning / diffusion models) as a whole, not manipulation in isolation on a fixed base. Rendering for ML, Not Just Physics: Push photorealistic rendering quality specifically to close the sim-to-real gap for vision-based loco-manipulation. Rendering fidelity is a first-class deliverable, not an afterthought on top of physics accuracy. Cross-Team Collaboration: Partner with the AI Research team on where simulated data and evaluation most accelerate loco-manipulation policy development, and with Data Ops on what's worth capturing in the real world vs. generating in sim. WHAT YOU'LL BRING - MS or PhD in CS/Robotics/Graphics, or equivalent hands-on experience. We care more about demonstrated depth building simulation/rendering systems than a fixed years-of-experience bar. - Hands-on experience with simulation stacks (MuJoCo, Isaac Sim/Isaac Lab, SAPIEN, Omniverse, Blender, or similar) for robotics or graphics. - Experience with procedural scene/asset generation, domain randomization, or photorealistic rendering pipelines at scale (not academic-scale one-off scenes). - Familiarity with training/evaluating manipulation or loco-manipulation policies (imitation learning, VLA, diffusion, or RL) and how simulated data actually feeds into them. - Experience simulating mobile bases (wheeled, tracked, or holonomic) is a real plus. Most of the field's simulation talent comes from fixed-arm tabletop manipulation or legged-humanoid balance/gait work, and neither maps cleanly onto a wheeled mobile manipulator. - Prior research or engineering background in loco-manipulation or whole-body control itself, coordinating mobile-base motion with arm manipulation, independent of any simulation-specific experienc…