We’re looking for a motivated research engineer to help build the next generation of AI-powered robots through high-fidelity simulation and large behavior models. You’ll work at the intersection of machine learning, computer vision, robotics, and physics-based simulation to enable scalable, generalizable robot policy development. Experience with simulation or embodied systems (robots, autonomous vehicles, etc.) is a strong plus.If our mission of revolutionizing robot learning through simulation and large behavior models resonates with you, we’d love to talk about how we can build it together.
Responsibilities
- Develop and manage physics-based robot simulation environments using Drake, enabling scalable training and evaluation of learning-based behavior models in realistic, physically grounded scenarios.
- Integrate and validate learned policies in simulation, assessing real-world applicability, generalization, and performance across diverse environments, tasks, geometries, and sensor viewpoints.
- Build, improve, and robustify end-to-end integrated ML pipelines for training multimodal (language, images, 3D, video, actions) models at scale.
- Train, fine-tune, and serve robot foundation models with a strong MLOps approach.
- Build processes for integrating collaboration-produced and open-source advancements and code into our internal stack.
- Collaborate with internal research scientists and our partner labs at top academic institutions and Toyota research labs to drive groundbreaking research at scale.
Qualifications
- 2+ years of professional engineering experience at an AI/ML-focused organization.
- Strong proficiency in Python and experience with simulation frameworks such as Drake, PyBullet, MuJoCo, or similar.
- Hands-on experience with robotics simulation, reinforcement learning, or large-scale machine learning.
- Familiarity with the latest methods in behavior learning and/or computer vision.
- Experience integrating ML models into simulated or real-world environments.
- Extensive practical experience with PyTorch.
- Ability to alternate between rapid prototyping and production-quality implementation.
- Demonstrated understanding of software engineering best practices, including testing, CI/CD, and documentation.
Bonus Qualifications
- Experience deploying models on embodied systems/robots.
- Experience working in mixed teams of research scientists and engineers.
- Exposure to MLOps tools and infrastructure (e.g., Docker, EC2, S3, Sagemaker).
- Experience with Bazel.
The pay range for this position at commencement of employment is expected to be between $152,000 and $228,000/year for California-based roles; however, base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. Note that TRI offers a generous benefits package (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave) and an annual cash bonus structure. Details of participation in these benefit plans will be provided if an employee receives an offer of employment.
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