About the role:

Where Frontier AI Meets Frontier Biology to Deliver Frontier Medicine

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.

You’ll operate at the intersection of:

• Frontier AI (representation learning, multimodal learning, alignment, evaluation)
• Messy biomedical reality (clinical endpoints, censoring, confounding, missingness, batch effects)
• Mechanism + translation (models that can be interrogated, stress-tested, and connected to biology and outcomes)

This role complements (not duplicates) the computational biology roles that focus on our program-facing biomarker analyses and trial decisions.  

What You Will Do

Foundation model development

• 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).
• Build model components that improve cross-modality grounding (e.g., aligning clinical narratives with molecular state and pathology signals).
• Develop robust approaches for missing-modality settings (train-time and inference-time), ensuring the OFM remains useful when only subsets of modalities exist.

Clinical + molecular fluency 

• Work with domain partners to define prediction targets and representation tests that matter: response, durability, toxicity, survival, progression, resistance, subtype stability, etc.
• Incorporate oncology-specific realities into modeling and evaluation (censoring, treatment lines, temporal leakage, cohort shift, annotation noise).

Evaluation, benchmarking, and scientific rigor

• Create evaluation harnesses that go beyond leaderboard metrics: ablations, cohort-shift tests, missingness stress tests, temporal generalization, calibration, and failure-mode analysis.
• Define and maintain benchmark suites that reflect Pathos priorities and are reproducible across model iterations.
• Partner with engineering to support scalable training/inference (multi-node GPU training, data pipelines, throughput optimization), while keeping scientific intent front-and-center 

Translation enablement 

• 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.
• Collaborate with computational biologists, translational scientists, and clinicians to ensure the OFM supports mechanism discovery and patient stratification workflows 

Who You Are

Minimum Qualifications

• 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.
• Deep hands-on experience with modern deep learning (PyTorch), including training large models and debugging optimization issues.
• Demonstrated ability to design representation learning / foundation model approaches and evaluate them rigorously (not just “train and report AUCs”).
• Comfort operating in ambiguous problem spaces with a bias toward execution and iteration.

Strongly Preferred 

• Multimodal foundation model experience (any of: clinical + omics, imaging + text, multimodal retrieval, alignment, late fusion/mixture-of-experts).
• Real experience with at least one of the following domains (enough to reason about the data-generating process and pitfalls):
• • Clinical text / EHR (notes, longitudinal events, coding systems, leakage traps)
  • Molecular/omics modeling (RNA/DNA/variant features, batch effects, multi-cohort generalization)
  • Pathology imaging (WSI feature learning, weak supervision, MIL, slide-level endpoints)

Nice to Have

• Distributed training and systems experience (FSDP/DeepSpeed, multi-node performance profiling)
• Experience with alignment methods (preference learning, instruction tuning, evaluation frameworks for reliability/robustness).
• Publications in relevant venues (NeurIPS/ICML/ICLR/ACL/MLHC) and/or impactful open-source work.
  
  

Location

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