AI Engineer (RL & WBC)
Indexed description
Our mission is to create advanced robots that can operate in complex environments, reducing human risk in conflict zones and enhancing efficiency in labor-intensive industries.
We are on the lookout for extraordinary engineers and scientists to join our team.
Your previous experience in robotics isn't a prerequisite — it's your talent and determination that truly count.
We expect that many of our team members will bring diverse perspectives from various industries and fields. We are looking for individuals with a proven record of exceptional ability and a history of creating things that work.
Our Culture
We like to be frank and honest about who we are, so that people can decide for themselves if this is a culture they resonate with. Please read more about our culture here https://foundation.bot/culture.
Who should join:
- You like working in person with a team in San Francisco.
- You deeply believe that this is the most important mission for humanity and needs to happen yesterday.
- You are highly technical - regardless of the role you are in. We are building technology; you need to understand technology well.
- You care about aesthetics and design inside out. If it's not the best product ever, it bothers you, and you need to “fix” it.
- You don't need someone to motivate you; you get things done.
- Design, develop, and optimize reinforcement learning algorithms for real-time control and locomotion of humanoid robots.
- Integrate learned policies into real-world robot platforms with hardware-in-the-loop validation.
- Collaborate with mechanical, perception, and embedded systems teams to ensure tight integration between hardware and software.
- Apply advanced techniques such as curriculum learning, domain randomization, and sim2real transfer to improve policy generalization.
- Analyze and optimize control performance with a focus on robustness, energy efficiency, and adaptability.
- Contribute to the continuous development of our in-house RL training pipelines and tooling.
- 2+ years of experience in machine learning (NNs, LVMs) and reinforcement learning applied to robotics or similar realtime environments.
- Hands-on experience with physics simulation environments (e.g., MuJoCo, Isaac Lab).
- Proficiency in Python and C++ for algorithm development and deployment.
- Experience with deep learning frameworks (e.g., PyTorch, JAX, TensorFlow).
- Familiarity with ROS/ROS2 and real-time robotic systems.
- strong software development experience, including CI/CD, unit testing, etc.
- Strong understanding of classical and modern control theory, locomotion dynamics, etc.
- Experience deploying RL algorithms on physical robots.
- Experience with high-performance computing for distributed training.
- Contributions to open-source RL, ML or robotics projects.
- M.Sc. or Ph.D. in Robotics, Computer Science, Mechanical Engineering, or a related field.
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