Machine Learning Engineer Intern - Planning

{
  "content_hash": "ef9ea4429981885870e09fc897d87be2888c4b5b3213a62ae210fbabfa257aec",
  "source_hash": "7365405c6dd840e0a369f1aa87aa1d3c6467644dab2e8e972985a7845e44c7d1",
  "last_changed_at": "2026-05-29T06:58:18.050Z",
  "active_status": "active"
}

{
  "language": "en",
  "location": {
    "raw": "Santa Clara, CA",
    "city": "Santa Clara",
    "region": "CA",
    "country": "United States",
    "is_remote": false,
    "confidence": 0.9
  },
  "salary_max": 65,
  "salary_min": 19,
  "inferred_at": "2026-06-06T07:56:17.734Z",
  "launch_scope": {
    "reason": "english_us_canada",
    "included": true,
    "language": "en",
    "location": {
      "raw": "Santa Clara, CA",
      "city": "Santa Clara",
      "region": "CA",
      "country": "United States",
      "is_remote": false,
      "confidence": 0.9
    },
    "countries": [
      "United States"
    ]
  },
  "remote_policy": null,
  "salary_period": "hour",
  "workplace_type": "on_site",
  "salary_currency": "USD"
}

{}

{
  "lists": [
    {
      "text": "Responsibilities:",
      "content": "\n<li><span data-sheets-root=\"1\">Multimodal Architecture Design: Modify and expand an existing Scene Transformer framework to generate a diverse set of ego-vehicle trajectories for ambiguous scenarios.</span></li>\n<li><span data-sheets-root=\"1\">Loss Function Engineering: Formulate and experiment with recursive, autoregressive, and multi-step losses to stabilize long-horizon trajectory rollouts.</span></li>\n<li><span data-sheets-root=\"1\">Diversity &amp; Feasibility Optimization: Mitigate mode collapse to ensure the generated paths are distinctly varied yet kinematically feasible.</span></li>\n<li><span data-sheets-root=\"1\">Evaluation &amp; Benchmarking: Design metrics to evaluate trajectory diversity, safety, and long-horizon accuracy against real-world driving logs.</span></li>\n"
    },
    {
      "text": "Required Skills:",
      "content": "\n<li><span data-sheets-root=\"1\">Expertise in trajectory prediction, behavior forecasting, and multi-agent motion modeling</span></li>\n<li><span data-sheets-root=\"1\">Experience training large-scale models with self-supervision, contrastive learning, representation learning, or scene-level modeling</span></li>\n<li><span data-sheets-root=\"1\">Practical experience with PyTorch, PyTorch Lightning or JAX.</span></li>\n<li><span data-sheets-root=\"1\">Experience with uncertainty estimation (aleatoric, epistemic), multi-modal prediction, and probabilistic modeling</span></li>\n<li><span data-sheets-root=\"1\">Understanding of AV stack components: localization, perception, tracking, prediction, and planning.</span></li>\n<li><span data-sheets-root=\"1\">Strong debugging, experiment design, data analysis, and failure-mode investigation skills.</span></li>\n"
    }
  ],
  "country": "US",
  "createdAt": 1779316802721,
  "updatedAt": null,
  "categories": {
    "team": "Planning",
    "location": "Santa Clara, CA",
    "commitment": "Intern",
    "department": "US Internships",
    "allLocations": [
      "Santa Clara, CA"
    ]
  },
  "salaryRange": {
    "max": 65,
    "min": 19,
    "currency": "USD",
    "interval": "per-hour-wage"
  },
  "workplaceType": "onsite"
}