Machine Learning Research Scientist, Mechanical Intuition in Multimodal Models

{
  "content_hash": "366aec20abd70f52ad25f02042728bfbd18486b3d178a1942995717403906c38",
  "source_hash": "6208662e18d1b6c14a38afd43f4798b8e4c95d210661ff3ef7e89171291f457b",
  "last_changed_at": "2026-05-29T07:01:10.652Z",
  "active_status": "active"
}

{
  "language": "en",
  "location": {
    "raw": "Los Altos, CA",
    "city": "Los Altos",
    "region": "CA",
    "country": "United States",
    "is_remote": false,
    "confidence": 0.9
  },
  "salary_max": null,
  "salary_min": null,
  "inferred_at": "2026-06-06T07:56:13.579Z",
  "launch_scope": {
    "reason": "english_us_canada",
    "included": true,
    "language": "en",
    "location": {
      "raw": "Los Altos, CA",
      "city": "Los Altos",
      "region": "CA",
      "country": "United States",
      "is_remote": false,
      "confidence": 0.9
    },
    "countries": [
      "United States"
    ]
  },
  "remote_policy": "hybrid",
  "salary_period": null,
  "workplace_type": "hybrid",
  "salary_currency": null
}

{}

{
  "lists": [
    {
      "text": "Responsibilities",
      "content": "<div>\n\n<li>Design and implement end-to-end modeling pipelines for machine assembly tasks, building from the ground up rather than adapting existing frameworks.</li>\n<li>Run systematic experiments to evaluate architectural variants, data collection and curation strategies, and a range of supervised and reinforcement learning techniques for physical manipulation.</li>\n<li>Develop and maintain rigorous evaluation protocols to measure policy performance across assembly scenarios, including generalization to novel parts, configurations, and failure modes.</li>\n<li>Explore how modern LLMs and agentic systems can be integrated to support physical reasoning and task planning in assembly contexts.</li>\n<li>Collaborate with researchers and engineers across TRI and Toyota's broader ecosystem to connect learning-based systems with real hardware and manufacturing workflows.</li>\n<li>Contribute to writing and publishing research results in peer-reviewed venues.</li>\n\n</div>"
    },
    {
      "text": "Qualifications",
      "content": "<div>\n\n<li>A PhD in a relevant field such as Computer Science, Robotics, Mechanical Engineering, or a related discipline, completed recently (or nearing completion), with some post-PhD or internship work experience.</li>\n<li>A demonstrated track record of implementing non-trivial learning systems — not just running baselines, but building pipelines and components from scratch.</li>\n<li>Hands-on experience with policy learning, reinforcement learning, or robot learning, with strong intuitions about what makes these approaches succeed or fail in practice.</li>\n<li>Proficiency in Python and comfort working across the full stack of a research project, from data processing to model training to evaluation.</li>\n<li>Genuine interest in how physical products are designed and manufactured.</li>\n\n</div>"
    },
    {
      "text": "Bonus Qualifications",
      "content": "<div>\n\n<li>Familiarity with large language models, vision-language models, or agentic AI frameworks, particularly in contexts involving structured reasoning or tool use.</li>\n<li>Experience with robot manipulation, motion planning, or sim-to-real transfer.</li>\n<li>Exposure to manufacturing processes, assembly planning, or CAD/CAM toolchains.</li>\n<li>Experience building or contributing to production-level research codebases.</li>\n\n</div>"
    }
  ],
  "country": "US",
  "createdAt": 1775156738162,
  "updatedAt": null,
  "categories": {
    "team": "Energy & Materials",
    "location": "Los Altos, CA; Cambridge, MA",
    "commitment": "Full-time",
    "department": "Energy & Materials",
    "allLocations": [
      "Los Altos, CA; Cambridge, MA"
    ]
  },
  "salaryRange": null,
  "workplaceType": "hybrid"
}