Hybrid Research Scientist, Generalist Embodied Agent Research

at NVIDIA

• Job title: Research Scientist, Generalist Embodied Agent Research
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: $160,000 - $299,000 per year
• Degree requirement: Postgraduate Degree
• Tech skills: Python, PyTorch, Tensorflow
• Location requirements: Hybrid, Santa Clara, United States

About the role

• Research Scientist in AI algorithms for humanoid robots at NVIDIA. Designing novel AI algorithms and collaborating with research teams on robotics and embodied agents.

Responsibilities

• Design and implement novel AI algorithms and models for general-purpose humanoid robots and embodied agents;
• Develop large-scale AI training and inference methods for foundation models;
• Optimize and deploy AI models in physical simulation and on robot hardware;
• Collaborate with research and engineering teams across all of NVIDIA to transfer research to products and services.

Requirements

• Ph.D. in Computer Science/Engineering, Electrical Engineering, etc., or equivalent research experience.
• Outstanding engineering skills in rapid prototyping and model training frameworks (PyTorch, Jax, Tensorflow, etc.).
• Python or C++ is required; CUDA or ROS proficiencies are a plus;
• Excellent knowledge and hands-on experience for training LLMs, multimodal foundation models, or large generative models.
• Experience in foundation and diffusion models, reinforcement learning, agent learning, and applied robotics.
• Experience across one or both of these fields: Multimodal Foundation Models.
• Hands-on training experience and publications in at least one of the following topics: LLMs; Large vision-language models; Video generative models and diffusion algorithms; Action-based transformers.
• Excellent skills in working with large-scale machine learning/AI systems and compute infrastructure.
• Robotics Hands-on training experience and publications in robot learning, such as reinforcement learning, imitation learning, classical control methods, etc.
• Deep understanding of robot kinematics, dynamics, and sensors;
• Ability to safely operate robot hardware, lab equipment, and tools;
• Knowledge of control methods, including PID, model predictive control, and whole-body control;
• Familiarity with physics simulation frameworks such as MuJoCo and Isaac Sim;
• Robot hardware design and hands-on building experience.

Benefits

• equity
• benefits