Principal Scientist — AI/ML for Life Sciences

We are a small, agile team building AI models to solve complex problems in biology and healthcare. As a Principal Scientist, you will own research problems end-to-end—from framing and prototyping to validation and deployment. You will anchor our technical efforts, transforming ambiguous biological questions into actionable ML solutions.

What you will Do

• End-to-End ML: Architect, train, and deploy production ML models tailored to complex life science datasets.
• Multi-Modal AI: Fuse omics, imaging, structural, and clinical data to drive biological discovery.
• Robust Modeling: Build resilient architectures designed to overcome small sample sizes, batch effects, and noisy data.
• Rigor & Validation: Establish rigorous benchmarks, evaluation metrics, and validation protocols.

Requirements

• Background: PhD + 5 yrs experience or MS + 10 yrs experience in CS, Computational Biology, Physics, or related field. Proven history of delivering production AI systems.
• Technical Expertise: Deep proficiency in multi-modal AI with hands-on experience in at least two: Transformers, GNNs, Diffusion/Flow-Matching, or SE(3)-equivariant models.
• Foundational ML: Mastery of self-supervised learning, representation learning, and fine-tuning for low-data regimes.
• Agentic Frameworks: Experience designing and implementing agentic workflows to automate complex research or decision‑making processes.
• Engineering: Fluent in Python and PyTorch/JAX; strong emphasis on clean, scalable code.
• Mindset: Thrives in ambiguous environments with rapid prototyping and concise cross‑domain communication.

Strongly preferred

• Experience with omics data (NGS, transcriptomics, proteomics).
• Expertise in MLOps, cloud infrastructure (AWS/GCP/Azure), and large-scale model training.
• Background in regulated or GxP environments.
• Collaboration: Partner with cross‑functional domain experts to translate scientific questions into tractable ML framing.
• Technical Leadership: Guide research strategy, mentor scientists, and uphold engineering best practices.
• Scientific Impact: Drive IP creation, high‑impact publications, and foundational strategy.