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Research Scientist, Generalist Embodied Agent Research - PhD College Grad

Job in Coos Bay, Coos County, Oregon, 97458, USA
Listing for: NVIDIA
Full Time position
Listed on 2026-01-14
Job specializations:
  • Engineering
    Robotics, AI Engineer, Artificial Intelligence
Salary/Wage Range or Industry Benchmark: 160000 - 299000 USD Yearly USD 160000.00 299000.00 YEAR
Job Description & How to Apply Below
Position: Research Scientist, Generalist Embodied Agent Research - PhD New College Grad 2026

Why consider this job opportunity

  • Base salary range of $160,000 - $299,000 depending on level and experience
  • Eligibility for equity and benefits
  • Work with a highly collaborative and innovative research team
  • Opportunity to contribute to groundbreaking projects in humanoid robotics and AI
  • Join a company recognized as one of the most desirable employers in the tech industry
  • Engage in work that has a significant impact on the future of autonomous technologies
What to Expect (Job Responsibilities)
  • Design and implement novel AI algorithms and models for 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 to transfer research to products and services
  • Conduct hands‑on training and experimentation with robot learning and AI systems
What is Required (Qualifications)
  • Ph.D. in Computer Science/Engineering, Electrical Engineering, or equivalent research experience
  • Outstanding engineering skills in rapid prototyping and model training frameworks (e.g., PyTorch, Jax, Tensor Flow)
  • Proficiency in Python or C++, with CUDA or ROS knowledge being a plus
  • Excellent knowledge and hands‑on experience in training LLMs, multimodal foundation models, or large generative models
  • Experience in foundation and diffusion models, reinforcement learning, agent learning, and applied robotics
How to Stand Out (Preferred Qualifications)
  • Hands‑on training experience and publications in LLMs, large vision‑language models, or video generative models
  • Deep understanding of robot kinematics, dynamics, and sensors
  • Familiarity with physics simulation frameworks such as Mu Jo Co  and Isaac Sim
  • Robot hardware design and hands‑on building experience
  • Knowledge of control methods, including PID, model predictive control, and whole‑body control

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