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About the Role
This is a hands-on research and engineering role at an early-stage AI data company focused on robotics and physical AI. You will work across learning algorithms, simulation, and hardware to push robot capabilities from simulation into real-world deployment, shaping how robots perceive, plan, and act.
Responsibilities
- Develop learning systems for robots, including policies, world models, and vision-language-action models.
- Build and maintain simulation environments and evaluation frameworks for robotic tasks.
- Apply and advance computer vision and perception algorithms for autonomous robots.
- Build pipelines connecting simulation and real-world robot deployment across real-to-sim and sim-to-real workflows.
- Work with robot hardware, sensors, actuators, and embedded systems to prototype and test capabilities.
- Train and evaluate reinforcement-learning and imitation-learning systems for manipulation, locomotion, or mobile platforms.
- Integrate robot platforms to validate algorithms in the physical world.
- Create tools and infrastructure that accelerate robotics research and development.
- Collaborate with external robotics companies and research labs on technical challenges.
Requirements
- Strong software skills in C++ and Python.
- Practical experience with deep learning, reinforcement learning, and computer vision.
- Demonstrated hands-on robotics research and engineering experience beyond academic prototypes.
- Real-to-sim and sim-to-real workflow experience.
- Hands-on mechanical and hardware engineering experience, including sensors, actuators, CAD, and embedded systems.
- Relevant industry experience in a company environment, not solely university research.
- Comfort operating as an individual contributor with end-to-end ownership in a small, fast-moving team.
- Background from a recognized company or strong university is preferred.
- Genuine robotics depth is required over adjacent ML experience alone.
- Candidates must be eligible to work in the US.
Skills
- C++
- Python
- Deep learning
- Reinforcement learning
- Computer vision
- Robotics
- Simulation platforms (Isaac Lab, MuJoCo, MJX, or Genesis are a plus)
- Mechanical engineering
- Hardware engineering
- Sensors
- Actuators
- CAD
- Embedded systems
- Multimodal AI approaches (transformers, diffusion models, world models, LLMs, or AI agents are a bonus)
Location
- San Francisco, California
Work Type
- On-site
Experience Level
- All experience levels welcome, from new graduates to seasoned researchers.
Salary/Compensations
- $100,000 to $300,000 annually (USD)