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Applied Scientist, Oncology Foundation Model (Intern)

Pathos · New York City, NY, United States · On Site · Active · $60–$80 / hour · Rippling ATS

Job facts

FieldValue
CompanyPathos
TitleApplied Scientist, Oncology Foundation Model (Intern)
Normalized title-
Department / teamEngineering
LocationNew York City, NY, United States
Work modelOn Site
Employment typeTemporary
Salary$60–$80 / hour
Statusactive
ATS providerRippling ATS
Posted / first seen2026-03-12 / 2026-05-29
Changed / last seen2026-06-06 / 2026-06-06

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Department jobsActive postings in Engineering.Open
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Linked records

CompanyPathos
Source2d66e9b5-1b63-475c-a03a-943980720722
ATS providerRippling ATS

Description

company Drug development shouldn’t be guesswork, not when patients are waiting. 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? 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. 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. How We Build 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 to 4 subject matter experts who each command hundreds of AI agents to do the work that used to require dozens of people. 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. 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. role About the Role The Oncology Foundation Model is the scientific core of Pathos, and this role sits at its center. We're hiring an Applied Scientist to lead the pretraining and post training of large language models purpose built for oncology, trained on a dataset unlike anything available in the public domain. This isn't a role where you fine tune general purpose models and call it done. You'll be making architectural decisions, designing evaluation frameworks, and working directly with oncologists and clinical researchers to ensure the model reflects real world medical reasoning. Your work will propagate through every AI system we build. If you want to do the most scientifically meaningful work of your career, at the intersection of frontier ML and cancer biology, this is where it happens. What You'll Do Lead the design, pretraining, and post training of large language models for oncology applications. Develop strategies for curating, processing, and governing oncology specific datasets at scale. Implement alignment techniques including RLHF, supervised fine tuning, and domain adaptation. Design rigorous evaluation frameworks to assess model performance, safety, and clinical relevance. Conduct novel research in LLM architectures and training methodologies for biomedical domains. Publish findings at top tier conferences and journals; communicate work to internal and external stakeholders. Partner with oncologists, clinical researchers, and cross functional teams throughout the model lifecycle. Mentor junior scientists and help build a culture of scientific rigor. Who You Are PhD in Computer Science, Machine Learning, AI, or a related field, or an MS with equivalent experience. Hands-on experience with deep learning and neural network architectures. Proven expertise in both pretraining and post training of large language models (e.g., LLaMA, Qwen, DeepSeek, or similar). Strong publication record at top tier venues: NeurIPS, ICML, ICLR, ACL, or EMNLP. Deep understanding of transformer architectures, attention mechanisms, and optimization. Proficient in Python and deep learning frameworks (PyTorch or TensorFlow/JAX). Experience with distributed training and large scale model infrastructure. Strong communicator, able to translate technical work for clinical and non technical audiences. Preferred Qualifications Experience applying LLMs to biomedical, healthcare, or life sciences domains. Background in computational biology, bioinformatics, or medical informatics. Knowledge of oncology terminology, clinical workflows, or cancer biology. Experience with retrieval augmented generation (RAG) or knowledge grounding techniques. Familiarity with model safety, alignment, and responsible AI practices. Track record of translating research into production systems. Experience with prompt engineering and instruction tuning. Contributions to open source ML projects. First author publications demonstrating research leadership. Location This is a hybrid role, requiring up to 3 to 4 days per week onsite at our NYC Headquarters.

Full job record

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Source ID2d66e9b5-1b63-475c-a03a-943980720722
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Provider Job Key939623e3-fe14-4d0e-b9f7-08c0411a060e
TitleApplied Scientist, Oncology Foundation Model (Intern)
Normalized Title—
Statusactive
Activeyes
Location TextNew York City, NY, United States
DepartmentEngineering
Team—
Employment Typetemporary
Workplace Typeon_site
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CountryUnited States
RegionNY
CityNew York City
Salary RawUSD 60-80 HOUR
Salary Min60
Salary Max80
Salary CurrencyUSD
Salary Periodhour
Source URLhttps://ats.rippling.com/pathos/jobs/939623e3-fe14-4d0e-b9f7-08c0411a060e
Apply URLhttps://ats.rippling.com/pathos/jobs/939623e3-fe14-4d0e-b9f7-08c0411a060e
First Seen At2026-05-29 07:13:53Z
Last Seen At2026-06-06 19:47:28Z
Last Checked At2026-06-06 19:47:28Z
Last Changed At2026-06-06 19:47:28Z
Inactive At—
Source Posted At2026-03-12 16:09:53Z
Source Updated At—
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      "role": "<meta><h2 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:17pt;font-weight:600;letter-spacing:0.5px;margin-top:18px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"color:rgb(26,26,26);font-size:17pt;white-space:pre-wrap;\">About the Role</strong></b></h2><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;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">The Oncology Foundation Model is the scientific core of Pathos, and this role sits at its center. We're hiring an Applied Scientist to lead the pretraining and post training of large language models purpose built for oncology, trained on a dataset unlike anything available in the public domain.</span></p><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;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">This isn't a role where you fine tune general purpose models and call it done. You'll be making architectural decisions, designing evaluation frameworks, and working directly with oncologists and clinical researchers to ensure the model reflects real world medical reasoning. Your work will propagate through every AI system we build.</span></p><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;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">If you want to do the most scientifically meaningful work of your career, at the intersection of frontier ML and cancer biology, this is where it happens.</span></p><h2 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:17pt;font-weight:600;letter-spacing:0.5px;margin-top:18px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"color:rgb(26,26,26);font-size:17pt;white-space:pre-wrap;\">What You'll Do</strong></b></h2><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(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Lead the design, pretraining, and post training of large language models for oncology applications.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Develop strategies for curating, processing, and governing oncology specific datasets at scale.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Implement alignment techniques including RLHF, supervised fine tuning, and domain adaptation.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Design rigorous evaluation frameworks to assess model performance, safety, and clinical relevance.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Conduct novel research in LLM architectures and training methodologies for biomedical domains.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Publish findings at top tier conferences and journals; communicate work to internal and external stakeholders.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Partner with oncologists, clinical researchers, and cross functional teams throughout the model lifecycle.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Mentor junior scientists and help build a culture of scientific rigor.</span></li></ul><h2 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:17pt;font-weight:600;letter-spacing:0.5px;margin-top:18px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"color:rgb(26,26,26);font-size:17pt;white-space:pre-wrap;\">Who You Are</strong></b></h2><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(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">PhD in Computer Science, Machine Learning, AI, or a related field, or an MS with equivalent experience.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Hands-on experience with deep learning and neural network architectures.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Proven expertise in both pretraining and post training of large language models (e.g., LLaMA, Qwen, DeepSeek, or similar).</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Strong publication record at top tier venues: NeurIPS, ICML, ICLR, ACL, or EMNLP.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Deep understanding of transformer architectures, attention mechanisms, and optimization.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Proficient in Python and deep learning frameworks (PyTorch or TensorFlow/JAX).</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Experience with distributed training and large scale model infrastructure.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Strong communicator, able to translate technical work for clinical and non technical audiences.</span></li></ul><h2 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:17pt;font-weight:600;letter-spacing:0.5px;margin-top:18px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"color:rgb(26,26,26);font-size:17pt;white-space:pre-wrap;\">Preferred Qualifications</strong></b></h2><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(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Experience applying LLMs to biomedical, healthcare, or life sciences domains.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Background in computational biology, bioinformatics, or medical informatics.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Knowledge of oncology terminology, clinical workflows, or cancer biology.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Experience with retrieval augmented generation (RAG) or knowledge grounding techniques.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Familiarity with model safety, alignment, and responsible AI practices.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Track record of translating research into production systems.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Experience with prompt engineering and instruction tuning.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">Contributions to open source ML projects.</span></li><li style=\"color:rgb(26,26,26);font-size:14pt;margin:3px 0px;letter-spacing:0.25px;line-height:1.6;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">First author publications demonstrating research leadership.</span></li></ul><h2 style=\"font-family:&quot;Basel Grotesk&quot;,Arial,sans-serif;line-height:1.6;font-size:17pt;font-weight:600;letter-spacing:0.5px;margin-top:18px;margin-bottom:4px;padding-left:0px;\"><b><strong style=\"color:rgb(26,26,26);font-size:17pt;white-space:pre-wrap;\">Location</strong></b></h2><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;\"><span style=\"color:rgb(26,26,26);font-size:14pt;white-space:pre-wrap;\">This is a hybrid role, requiring up to 3 to 4 days per week onsite at our NYC Headquarters.</span></p><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;\"><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=\"color:rgb(0,0,0);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></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;\">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></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;\">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></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=\"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></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><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;\"><b><strong style=\"color:rgb(0,0,0);font-size:12pt;white-space:pre-wrap;\">How We Build</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;\">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 to 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></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;\">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></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=\"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:11pt;font-weight:400;line-height:1.6;letter-spacing:0.25px;margin:4px 0px;padding:0px;\"><br></p>"
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