Founding Scientist, Machine Learning & Computational Biology

Backed by leading investors, an early-stage biotech is building machine learning systems to tackle complex biological problems. The team combines deep expertise in modern ML and computational biology to create models that deliver interpretable, decision-ready insights.

This is an opportunity to join as one of the first hires and shape the technical foundation of a company redefining how biology and computation intersect.

Key Responsibilities

• Design and implement ML models for biological sequence and assay data.
• Develop architectures that incorporate uncertainty estimation, calibration, and interpretability.
• Translate open-ended biological questions into tractable modeling programs: define surrogate objectives, sampling strategies, and validation frameworks.
• Establish rigorous evaluation protocols beyond headline metrics, focusing on precision, recall, and error analysis.
• Partner with scientists to ensure outputs are biologically meaningful and actionable.
• Contribute to technical roadmap and mentor future hires.

Ideal Profile

• 4+ years in ML, computational biology, or related fields; strong Python and deep learning experience (PyTorch preferred).
• Ph.D. with a strong publication record in top ML or computational biology venues.
• Proven ability to deliver models that generalize under noisy or incomplete biological data.
• Comfortable working cross-functionally and communicating trade-offs clearly.
• Publications or shipped work in ML and/or bioinformatics that demonstrate cross-functional impact (rigor over venue prestige).
• Bonus: Experience with pMHC/MAP, Bayesian deep learning, or geometric modeling.
• Bonus: Prior startup experience or evidence of thriving in ambiguous, fast-moving environments.

If you want to work at the frontier of ML and biology and thrive in an early-stage setting where creativity and pragmatism matter, we'd love to hear from you.