Applied Scientist, Oncology Foundation Model; Intern

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.

• 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, Deep Seek, 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 Tensor Flow/JAX).
• Experience with distributed training and large scale model infrastructure.
• Strong communicator, able to translate technical work for clinical and non technical audiences.

• 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.

This is a hybrid role, requiring up to 3 to 4 days per week onsite at our NYC Headquarters.