Pathos · 7 hours ago
Senior/Staff Applied Scientist, Multimodal Representation Learning (Oncology)
New York, NY
Full-time
Hybrid
Senior Level, Lead/Staff
$150K/yr - $200K/yrPathos is building a next-generation biotech with AI at the core, aiming to redefine drug development in oncology. The role involves developing advanced multimodal representation learning strategies to enhance drug discovery and patient outcomes.
Artificial Intelligence (AI)BiotechnologyLife ScienceMedicalTherapeutics
Responsibilities
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
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)
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
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
Qualification
Required
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
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)
Company
Pathos
Pathos is a biotechnology company that leverages AI to accelerate oncology drug development.
Founded in 2020
11-50 employees
H1B Sponsorship
Pathos has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (1)
2023 (1)
Funding
Current Stage
Late StageTotal Funding
$447MKey Investors
New Enterprise Associates
2025-05-15Series D· $365M
2024-10-29Series C· $62M
2023-03-02Series Unknown· $20M
Leadership Team
Ryan Fukushima
Co-Founder
Recent News
2025-10-27
2025-09-19
Company data provided by crunchbase