Research Engineer, Atlas Physics Simulation for RL
Boston Dynamics
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Waltham, MA
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Not Applicable
Posted April 5, 2026
Job link
Thinking about this job
Responsibilities
Commitments
Responsibilities
- Develop large scale physics simulation that can efficiently train reinforcement learning agents
- Evaluate and benchmark different physics simulators, mesh representations and rendering pipelines
- Scale physics simulations to generate millions of samples per second
- Build synthetic rendering pipelines to create photorealistic images
- Extend existing physics simulators to improve simulation quality
Commitments
The chance to help define what’s possible in real-world robotics
Not Met Priorities
What still needs stronger evidence
Requirements
- Extend existing physics simulators to improve simulation quality
- Detailed understanding physics simulation including contact solvers and mesh representations
- Extensive experience with physics simulation including MuJoCo, IsaacSim and Warp
- Experience with rendering pipelines to create photorealistic synthetic images
- Strong foundation in Python, C++ and modern numerical frameworks (e.g., PyTorch and Jax)
- Experience in algorithm design, debugging, and performance optimization
- The Ideal Candidate Has
- A PhD or equivalent research experience in reinforcement learning or robotic manipulation
- Publications at top tier robotics venues including RSS, CoRL, Science Robotics, ICRA
- Experience training RL policies in simulation for robots or simulated characters
- Strong understanding of the advantages and disadvantages of gpu based simulators
- Understanding of GPU and CPU compute architectures
- Experience with heterogeneous compute clusters, kubernetes and docker
Preferred Skills
- Publications at top tier robotics venues including RSS, CoRL, Science Robotics, ICRA
- Experience training RL policies in simulation for robots or simulated characters
- Strong understanding of the advantages and disadvantages of gpu based simulators
- Understanding of GPU and CPU compute architectures
- Experience with heterogeneous compute clusters, kubernetes and docker
Education
- (Not required) – MS with 3 years of industry experience or PhD in Computer Science, Machine Learning, Robotics, or a related field
- (Not required) – A PhD or equivalent research experience in reinforcement learning or robotic manipulation
Are you passionate about using physics simulation, reinforcement learning and humanoid robots? Curious what you’d be able to accomplish with total access to Boston Dynamics robots? As a Research Engineer working on physics simulation on the Atlas Behavior Learning team, you will join a world-class team of engineers and scientists focused on creating groundbreaking mobile manipulation behaviors for humanoids.
We are investing in reinforcement learning and physics simulation as a key technology for achieving dexterous and robust whole-body manipulation that can be deployed in real-world environments.
In This Role, You Will Be Responsible For
Develop large scale physics simulation that can efficiently train reinforcement learning agents
Evaluate and benchmark different physics simulators, mesh representations and rendering pipelines
Scale physics simulations to generate millions of samples per second
Build synthetic rendering pipelines to create photorealistic images
Extend existing physics simulators to improve simulation quality
We Are Looking For
MS with 3 years of industry experience or PhD in Computer Science, Machine Learning, Robotics, or a related field
Detailed understanding physics simulation including contact solvers and mesh representations
Extensive experience with physics simulation including MuJoCo, IsaacSim and Warp
Experience with rendering pipelines to create photorealistic synthetic images
Strong foundation in Python, C++ and modern numerical frameworks (e.g., PyTorch and Jax)
Experience in algorithm design, debugging, and performance optimization
The Ideal Candidate Has
A PhD or equivalent research experience in reinforcement learning or robotic manipulation
Publications at top tier robotics venues including RSS, CoRL, Science Robotics, ICRA
Experience training RL policies in simulation for robots or simulated characters
Strong understanding of the advantages and disadvantages of gpu based simulators
Understanding of GPU and CPU compute architectures
Experience with heterogeneous compute clusters, kubernetes and docker
Why join us?
Direct access to cutting-edge robots and the infrastructure to run large-scale experiments
A collaborative, mission-driven team where your ideas have real impact
The chance to help define what’s possible in real-world robotics
We are investing in reinforcement learning and physics simulation as a key technology for achieving dexterous and robust whole-body manipulation that can be deployed in real-world environments.
In This Role, You Will Be Responsible For
Develop large scale physics simulation that can efficiently train reinforcement learning agents
Evaluate and benchmark different physics simulators, mesh representations and rendering pipelines
Scale physics simulations to generate millions of samples per second
Build synthetic rendering pipelines to create photorealistic images
Extend existing physics simulators to improve simulation quality
We Are Looking For
MS with 3 years of industry experience or PhD in Computer Science, Machine Learning, Robotics, or a related field
Detailed understanding physics simulation including contact solvers and mesh representations
Extensive experience with physics simulation including MuJoCo, IsaacSim and Warp
Experience with rendering pipelines to create photorealistic synthetic images
Strong foundation in Python, C++ and modern numerical frameworks (e.g., PyTorch and Jax)
Experience in algorithm design, debugging, and performance optimization
The Ideal Candidate Has
A PhD or equivalent research experience in reinforcement learning or robotic manipulation
Publications at top tier robotics venues including RSS, CoRL, Science Robotics, ICRA
Experience training RL policies in simulation for robots or simulated characters
Strong understanding of the advantages and disadvantages of gpu based simulators
Understanding of GPU and CPU compute architectures
Experience with heterogeneous compute clusters, kubernetes and docker
Why join us?
Direct access to cutting-edge robots and the infrastructure to run large-scale experiments
A collaborative, mission-driven team where your ideas have real impact
The chance to help define what’s possible in real-world robotics