Research Scientist, Simulation
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Before you apply
SalaryNo pay range posted
LocationRedwood City, CA
Work modeon-site
Typefull-time
SenioritySenior
DepartmentResearch
Company size51–200 people
PostedAug 4, 2026 · 3mo ago
Verified live19h ago
May no longer be actively hiring
Posted by the company on Aug 4, 202672% of this company's open roles have been listed for 90+ daysNo pay range listed, though California requires one
At a glanceSummarised by Seekless from the posting.
MS or PhD in CS/Robotics/Graphics, or equivalent hands-on experience
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
Familiarity with training/evaluating manipulation or loco-manipulation policies (imitation learning, VLA, diffusion, or RL)
Prior research or engineering background in loco-manipulation or whole-body control itself
Strong Python skills
Comfort with PyTorch or JAX for anything touching model training/eval
Senior enough to define your own research agenda and exercise independent judgment on where simulation adds the most leverage
Experience simulating mobile bases (wheeled, tracked, or holonomic) is a real plus
Experience with real-to-sim-to-real pipelines (3D reconstruction, NeRF/Gaussian splatting, differentiable rendering)
Exposure to world-model / video-prediction research (e.g. learned dynamics or latent world models)
GPU-scale physics simulation experience (CUDA, large-batch parallel sim)
Background simulating wheeled/mobile manipulators specifically
Publications at CoRL, RSS, ICRA, NeurIPS, CVPR, or SIGGRAPH
Experience leading or mentoring within a small research team
Skills
Python
PyTorch
JAX
MuJoCo
Isaac Sim
Isaac Lab
SAPIEN
Omniverse
Blender
CUDA
About the company
Dyna Robotics builds general-purpose robots powered by a proprietary embodied AI foundation model with top-in-industry generalization and real-world performance. Already deployed with customers across multiple industries, our robots do commercial-grade work in the physical world. Our team comes from Google DeepMind, Meta, and Cruise, and we’re backed by CRV, First Round, and other leading investors.
About the role
As a Research 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.
This is a full-time, on-site position in Redwood City.
What You’ll Do
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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.
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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.
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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.
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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.
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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.
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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
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Education: 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.
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Simulation Stacks: Hands-on experience with simulation stacks (MuJoCo, Isaac Sim/Isaac Lab, SAPIEN, Omniverse, Blender, or similar) for robotics or graphics.
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Procedural Generation & Rendering: Experience with procedural scene/asset generation, domain randomization, or photorealistic rendering pipelines at scale (not academic-scale one-off scenes).
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Policy Training & Evaluation: Familiarity with training/evaluating manipulation or loco-manipulation policies (imitation learning, VLA, diffusion, or RL) and how simulated data actually feeds into them.
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Mobile Base Simulation: 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.
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Loco-Manipulation Background: 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 experience.
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Technical Stack: Strong Python skills; comfort with PyTorch or JAX for anything touching model training/eval.
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Seniority & Ownership: Senior enough to define your own research agenda and exercise independent judgment on where simulation adds the most leverage. This is closer to a founding-type ownership role than a narrow IC slot on an existing pipeline.
Bonus Points For
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Real-to-Sim-to-Real: Experience with real-to-sim-to-real pipelines (3D reconstruction, NeRF/Gaussian splatting, differentiable rendering).
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World Models: Exposure to world-model / video-prediction research (e.g. learned dynamics or latent world models) as a complement to classical physics simulation.
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GPU-Scale Simulation: GPU-scale physics simulation experience (CUDA, large-batch parallel sim) rather than single-instance sim.
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Mobile Manipulator Simulation: Background simulating wheeled/mobile manipulators specifically (e.g. Boston Dynamics Stretch, warehouse/logistics robotics) rather than legged humanoids or fixed-base arms.
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Publications: Publications at CoRL, RSS, ICRA, NeurIPS, CVPR, or SIGGRAPH.
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Team Leadership: Experience leading or mentoring within a small research team.
Legal
At Dyna Robotics, we build technology for the real world, which requires a team as diverse as the environments our robots inhabit. We are an equal opportunity employer committed to technical rigor and mutual respect.
Don’t let a checklist stop you. Data shows that underrepresented groups often only apply if they meet 100% of the criteria. We value problem-solving and grit over keyword matching. If you’re passionate about robotics, no matter your discipline, we want to hear from you, even if you don’t check every box.