Senior/Staff Applied Scientist, Multimodal Representation Learning (Oncology)

{
  "content_hash": "5e07a823bb2cb844c1320998a1eeeb69e6ae23a30d3ef663f54047908570f3f8",
  "source_hash": "32c5d715b6b69afe439b7ddec44d1bd2c9e649d8cc3559bb13df20371287067d",
  "last_changed_at": "2026-06-06T19:47:28.755Z",
  "active_status": "active"
}

{
  "language": "en-us",
  "location": {
    "raw": "New York City, NY, United States",
    "city": "New York City",
    "region": "NY",
    "country": "United States",
    "is_remote": false,
    "confidence": 0.98,
    "workplace_type": "on_site"
  },
  "salary_max": 200000,
  "salary_min": 150000,
  "inferred_at": "2026-06-06T19:47:28.749Z",
  "launch_scope": {
    "reason": "english_us_canada",
    "included": true,
    "language": "en-us",
    "location": {
      "raw": "New York City, NY, United States",
      "city": "New York City",
      "region": "NY",
      "country": "United States",
      "is_remote": false,
      "confidence": 0.98,
      "workplace_type": "on_site"
    },
    "countries": [
      "United States"
    ]
  },
  "remote_policy": null,
  "salary_period": "year",
  "workplace_type": "on_site",
  "salary_currency": "USD"
}

{}

{
  "list_job": {
    "id": "b3541860-e5ba-41c0-a050-1b830e6b238d",
    "url": "https://ats.rippling.com/pathos/jobs/b3541860-e5ba-41c0-a050-1b830e6b238d",
    "name": "Senior/Staff Applied Scientist, Multimodal Representation Learning (Oncology)",
    "language": "en-US",
    "locations": [
      {
        "city": "New York City",
        "name": "New York City, NY",
        "state": "New York",
        "country": "United States",
        "stateCode": "NY",
        "countryCode": "US",
        "workplaceType": "ON_SITE"
      }
    ],
    "department": {
      "name": "Computational Biology"
    }
  },
  "detail_job": {
    "url": "https://ats.rippling.com/pathos/jobs/b3541860-e5ba-41c0-a050-1b830e6b238d",
    "name": "Senior/Staff Applied Scientist, Multimodal Representation Learning (Oncology)",
    "uuid": "b3541860-e5ba-41c0-a050-1b830e6b238d",
    "board": {
      "logo": {
        "url": "https://prod-images.rippling.com/75cf13a9ba76879beceafbfd6df8b524f5ae71d0.png?Expires=1780861648&Signature=Y8RtMl-MmnGidTjgxEvmNQaoga9GUziA8XXAQeD-jnrE5ESzKGneGdmTh3wq9PmxXs~~BXu0MmatY9KC3nfG9aOyiA0lFvLsr77PpXbxpz~AvEepRGmdKVqPo4yDniWQu12skCA2hCJva0mgSScuuQHyfounmTpelMimkevY-veRb1AM~OyDfUuVOoH4zbmCRoypfoqL7jH8DspCDG--tl40MBiylvBvHs8oiMvP5tBnfcP9QqIF5~WFbJbidab-pc300odNwP~BH3MOsNTeK6TRTRCqhoo~m1XrkHjqDFGNPj-GaBiMo38uYzlZ95zL4dSGdtnMQTra61f8sbZeFQ__&Key-Pair-Id=K2Y26R2ZPP26PH",
        "name": "logo file-02.png",
        "type": "image/png"
      },
      "slug": "pathos",
      "title": "Pathos",
      "banner": {
        "url": null,
        "name": "",
        "type": ""
      },
      "boardURL": "https://ats.rippling.com/pathos/jobs",
      "fontType": null,
      "subtitle": "Open Positions",
      "boardType": "RIPPLING",
      "linkColor": null,
      "buttonColor": null,
      "legalNotice": null,
      "buttonTextColor": null,
      "noOpeningsMessage": null,
      "groupJobsByLocation": false,
      "showBoardLogoOnJobPost": false,
      "showCompanyInfoUnderJobPost": false
    },
    "createdOn": "2026-01-29T13:26:36.058000-08:00",
    "department": {
      "name": "Computational Biology",
      "base_department": "Computational Biology",
      "department_tree": [
        "Computational Biology"
      ]
    },
    "companyName": "Pathos",
    "description": {
      "role": "<meta><h2 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:14pt;font-weight:600;letter-spacing:0.5px;margin-top:18px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:14pt;white-space:pre-wrap;\">Where Frontier AI Meets Frontier Biology to Deliver Frontier Medicine</strong></b></h2><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">We are hiring specialized scientists to accelerate development of our Oncology Foundation Model (OFM) stack. This is not a generic “model tinkering” role. The person in this seat will help define and build the modeling strategy that turns multimodal oncology data (clinical text/EHR, genomics, transcriptomics, pathology imaging, and derived features) into useful representations and predictive capabilities that directly support drug discovery and development.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">You’ll operate at the intersection of:</span></p><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Frontier AI (representation learning, multimodal learning, alignment, evaluation)</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Messy biomedical reality (clinical endpoints, censoring, confounding, missingness, batch effects)</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Mechanism + translation (models that can be interrogated, stress-tested, and connected to biology and outcomes)</span></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">This role complements (not duplicates) the computational biology roles that focus on our program-facing biomarker analyses and trial decisions.&nbsp;&nbsp;</span></p><h2 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:14pt;font-weight:600;letter-spacing:0.5px;margin-top:18px;margin-bottom:4px;text-align:justify;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:14pt;white-space:pre-wrap;\">What You Will Do</strong></b></h2><h3 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:12pt;font-weight:600;letter-spacing:0.25px;margin-top:14px;margin-bottom:4px;text-align:justify;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Foundation model development</strong></b></h3><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Design and implement multimodal pretraining and fine-tuning strategies for oncology data (e.g., contrastive objectives, masked modeling, multitask learning, retrieval-augmented training, late/early fusion variants).</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Build model components that improve cross-modality grounding (e.g., aligning clinical narratives with molecular state and pathology signals).</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Develop robust approaches for missing-modality settings (train-time and inference-time), ensuring the OFM remains useful when only subsets of modalities exist.</span></li></ul><h3 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:12pt;font-weight:600;letter-spacing:0.25px;margin-top:14px;margin-bottom:4px;text-align:justify;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Clinical + molecular fluency&nbsp;</strong></b></h3><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Work with domain partners to define prediction targets and representation tests that matter: response, durability, toxicity, survival, progression, resistance, subtype stability, etc.</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Incorporate oncology-specific realities into modeling and evaluation (censoring, treatment lines, temporal leakage, cohort shift, annotation noise).</span></li></ul><h3 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:12pt;font-weight:600;letter-spacing:0.25px;margin-top:14px;margin-bottom:4px;text-align:justify;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Evaluation, benchmarking, and scientific rigor</strong></b></h3><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Create evaluation harnesses that go beyond leaderboard metrics: ablations, cohort-shift tests, missingness stress tests, temporal generalization, calibration, and failure-mode analysis.</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Define and maintain benchmark suites that reflect Pathos priorities and are reproducible across model iterations.</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Partner with engineering to support scalable training/inference (multi-node GPU training, data pipelines, throughput optimization), while keeping scientific intent front-and-center&nbsp;</span></li></ul><h3 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:12pt;font-weight:600;letter-spacing:0.25px;margin-top:14px;margin-bottom:4px;text-align:justify;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Translation enablement&nbsp;</strong></b></h3><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Package model outputs so they can be consumed by internal science teams: embeddings, uncertainty estimates, interpretable signals, retrieval tools, and model cards that clearly state what’s reliable vs. not.</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Collaborate with computational biologists, translational scientists, and clinicians to ensure the OFM supports mechanism discovery and patient stratification workflows&nbsp;</span></li></ul><h2 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:14pt;font-weight:600;letter-spacing:0.5px;margin-top:18px;margin-bottom:4px;text-align:justify;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:14pt;white-space:pre-wrap;\">Who You Are</strong></b></h2><h3 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:12pt;font-weight:600;letter-spacing:0.25px;margin-top:14px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Minimum Qualifications</strong></b></h3><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Advanced degree (PhD strongly preferred) in ML/AI, CS, Statistics, Computational Biology, Bioinformatics, or a related field, or equivalent industry experience with a strong publication/impact record.</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Deep hands-on experience with modern deep learning (PyTorch), including training large models and debugging optimization issues.</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Demonstrated ability to design representation learning / foundation model approaches and evaluate them rigorously (not just “train and report AUCs”).</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Comfort operating in ambiguous problem spaces with a bias toward execution and iteration.</span></li></ul><h3 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:12pt;font-weight:600;letter-spacing:0.25px;margin-top:14px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Strongly Preferred&nbsp;</strong></b></h3><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Multimodal foundation model experience (any of: clinical + omics, imaging + text, multimodal retrieval, alignment, late fusion/mixture-of-experts).</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Real experience with at least one of the following domains (enough to reason about the data-generating process and pitfalls):</span></li><li style=\"font-size:12pt;--listitem-marker-color:#000000;list-style:none;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><ul data-pattern=\"discCircleSquare\" data-depth=\"2\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin-left:0px;margin-right:0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:circle;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Clinical text / EHR (notes, longitudinal events, coding systems, leakage traps)</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Molecular/omics modeling (RNA/DNA/variant features, batch effects, multi-cohort generalization)</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Pathology imaging (WSI feature learning, weak supervision, MIL, slide-level endpoints)</span></li></ul></li></ul><h3 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:12pt;font-weight:600;letter-spacing:0.25px;margin-top:14px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Nice to Have</strong></b></h3><ul data-pattern=\"discCircleSquare\" data-depth=\"1\" style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:11pt;font-weight:400;margin:8px 0px;line-height:1.6;padding:0px 0px 0px 32px;list-style-type:disc;\"><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Distributed training and systems experience (FSDP/DeepSpeed, multi-node performance profiling)</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Experience with alignment methods (preference learning, instruction tuning, evaluation frameworks for reliability/robustness).</span></li><li style=\"color:rgb(0,0,0);font-size:12pt;--listitem-marker-color:#000000;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">Publications in relevant venues (NeurIPS/ICML/ICLR/ACL/MLHC) and/or impactful open-source work.</span><br><br></li></ul><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:14pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><b><strong style=\"color:rgb(0,0,0);font-size:14pt;white-space:pre-wrap;\">Location</strong></b></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">This is a hybrid role, requiring up to 3 days per week onsite, in our NYC Headquarters.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p>",
      "company": "<meta><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><span style=\"font-size:12pt;white-space:pre-wrap;\">Drug development shouldn’t be guesswork, not when patients are waiting.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br><span style=\"font-size:12pt;white-space:pre-wrap;\">Pathos is building a next-generation biotech with AI at the core. Not as a feature, but as the operating system for how medicines get developed. We believe most drugs don’t fail because the science was wrong. They fail because they were tested in the wrong patients, with the wrong assumptions, in trials that couldn’t answer the real question: who benefits, and why?</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br><span style=\"font-size:12pt;white-space:pre-wrap;\">Pathos exists to change that. We’re building the largest foundation model in oncology and pairing it with proprietary AI systems, deep oncology expertise, and 200+ petabytes of multimodal data linked to patient outcomes, so we can make development decisions with more precision, much earlier.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br><span style=\"font-size:12pt;white-space:pre-wrap;\">This is not theoretical. We’re well-capitalized and have the leadership to build a generational company. We invest in and advance our own clinical-stage programs, using our AI platform to sharpen trial design, patient selection and biomarker strategy. So therapies reach the patients most likely to benefit, sooner.</span><br><br><b><strong style=\"font-size:12pt;white-space:pre-wrap;\">How We Build</strong></b><br><span style=\"font-size:12pt;white-space:pre-wrap;\">Pathos does not operate like a traditional biotech. There is no middle management. There are no layers of approval. The company is designed, from the ground up, around small teams of 2–4 subject-matter experts who each command hundreds of AI agents to do the work that used to require dozens of people.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br><span style=\"font-size:12pt;white-space:pre-wrap;\">Everyone builds. Everyone ships. Every function at Pathos — from clinical execution to asset selection to the foundation model itself — runs on this model. Our product velocity delivers meaningful outcomes in hours instead of weeks. This is not a future aspiration. It is how we operate today.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br><span style=\"font-size:12pt;white-space:pre-wrap;\">The people who thrive here are operators: deep experts who can specify what needs to happen, orchestrate AI agents to execute at scale, and make high-judgment calls that compound over time. If you have spent your career building and shipping AI systems at scale, this is the environment where that experience becomes a superpower.</span></p><p style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;font-size:12pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p>"
    },
    "workLocations": [
      "New York City, NY"
    ],
    "employmentType": {
      "id": "Salaried, full-time",
      "label": "SALARIED_FT"
    },
    "payRangeDetails": [
      {
        "currency": "USD",
        "isRemote": false,
        "location": "New York Office",
        "rangeEnd": 200000,
        "frequency": "YEAR",
        "rangeStart": 150000
      }
    ],
    "activeJobApplication": {
      "basicQuestions": [
        {
          "oid": "first_name",
          "title": "First name",
          "required": true,
          "fieldType": "SHORT_ANSWER"
        },
        {
          "oid": "last_name",
          "title": "Last name",
          "required": true,
          "fieldType": "SHORT_ANSWER"
        },
        {
          "oid": "email",
          "title": "Email",
          "required": true,
          "fieldType": "SHORT_ANSWER"
        },
        {
          "oid": "pronouns",
          "title": "Pronouns",
          "required": false,
          "fieldType": "PRONOUN"
        },
        {
          "oid": "current_company",
          "title": "Current company",
          "required": false,
          "fieldType": "SHORT_ANSWER"
        },
        {
          "oid": "phone_number",
          "title": "Phone number",
          "required": true,
          "fieldType": "PHONE_NUMBER"
        },
        {
          "oid": "location",
          "title": "Location (city only)",
          "required": true,
          "fieldType": "SHORT_ANSWER"
        },
        {
          "oid": "resume",
          "title": "Resume",
          "required": true,
          "fieldType": "FILE"
        },
        {
          "oid": "cover_letter",
          "title": "Cover letter",
          "required": false,
          "fieldType": "FILE"
        }
      ],
      "customQuestions": {
        "fields": [
          {
            "oid": "first_name",
            "title": "First name",
            "required": true,
            "fieldData": {},
            "fieldType": "SHORT_ANSWER"
          },
          {
            "oid": "last_name",
            "title": "Last name",
            "required": true,
            "fieldData": {},
            "fieldType": "SHORT_ANSWER"
          },
          {
            "oid": "email",
            "title": "Email",
            "required": true,
            "fieldData": {},
            "fieldType": "SHORT_ANSWER"
          },
          {
            "oid": "pronouns",
            "title": "Pronouns",
            "required": false,
            "fieldData": {},
            "fieldType": "PRONOUN"
          },
          {
            "oid": "current_company",
            "title": "Current company",
            "required": false,
            "fieldData": {},
            "fieldType": "SHORT_ANSWER"
          },
          {
            "oid": "phone_number",
            "title": "Phone number",
            "required": true,
            "fieldData": {},
            "fieldType": "PHONE_NUMBER"
          },
          {
            "oid": "location",
            "title": "Location (city only)",
            "required": true,
            "fieldData": {},
            "fieldType": "SHORT_ANSWER"
          },
          {
            "oid": "resume",
            "title": "Resume",
            "required": true,
            "fieldData": {},
            "fieldType": "FILE"
          },
          {
            "oid": "cover_letter",
            "title": "Cover letter",
            "required": false,
            "fieldData": {},
            "fieldType": "FILE"
          }
        ]
      },
      "additionalQuestions": [
        {
          "id": "6a0dddce5a831be6795c1ef4",
          "form": {
            "sections": [],
            "questions": [
              {
                "tags": [],
                "title": "Are you authorized to work in the United States for any employer?",
                "canEdit": false,
                "dataType": "enum",
                "isPrivate": false,
                "uniqueKey": "f6ea6520-606c-4b98-9f34-d2c7e6070465",
                "intChoices": [],
                "isRequired": true,
                "strChoices": [
                  "Yes",
                  "No"
                ],
                "description": "",
                "questionType": "SINGLE_SELECT_DROPDOWN",
                "allowComments": false,
                "isOtherEnabled": false,
                "isMultiSelectEnabled": false
              },
              {
                "tags": [],
                "title": "Will you now or in the future require visa sponsorship?",
                "canEdit": false,
                "dataType": "enum",
                "isPrivate": false,
                "uniqueKey": "7c22ab4c-4058-44fd-b118-4aa69fdf93f0",
                "intChoices": [],
                "isRequired": true,
                "strChoices": [
                  "Yes",
                  "No"
                ],
                "questionType": "SINGLE_SELECT_DROPDOWN",
                "allowComments": false,
                "isOtherEnabled": false,
                "isMultiSelectEnabled": false
              }
            ],
            "skipLogic": [],
            "deletedSections": [],
            "deletedQuestions": []
          },
          "name": "Work Authorization"
        },
        {
          "id": "6a0dddce5a831be6795c1ef5",
          "form": {
            "sections": [],
            "questions": [
              {
                "tags": [],
                "title": "This role is based in our NYC HQ with an onsite requirement of at least 3 days per week.  We are unable to offer relocation assistance presently.  If you don't currently live in a commutable range, would you relocate without company assistance?",
                "canEdit": false,
                "dataType": "enum",
                "isPrivate": false,
                "uniqueKey": "d8e9d4af-cce9-4c16-83cf-32cca6490045",
                "intChoices": [],
                "isRequired": true,
                "strChoices": [
                  "Yes",
                  "No"
                ],
                "description": "",
                "questionType": "SINGLE_SELECT_DROPDOWN",
                "allowComments": true,
                "isOtherEnabled": false,
                "isMultiSelectEnabled": false
              }
            ],
            "skipLogic": [],
            "deletedSections": [],
            "deletedQuestions": []
          },
          "name": "NYC Location question 3 days"
        }
      ]
    },
    "hasAIEvaluationsEnabled": false,
    "eeocQuestionnaireEnabled": true,
    "applicationConfirmationTemplate": "655be3f8334869f2386920eb",
    "eeocQuestionnaireEnabledForJobPost": true
  },
  "detail_meta": {
    "url": "https://ats.rippling.com/api/v2/board/pathos/jobs/b3541860-e5ba-41c0-a050-1b830e6b238d",
    "http_status": 200,
    "content_type": "application/json",
    "response_bytes": 25328
  },
  "detail_errors": []
}