Senior ML Scientist (Biological Systems)

Location: San Francisco, CA, USA

Salary: $268,000–$358,000 USD base salary per year, with bonus potential and early-stage equity.

Company

hirify.global develops AI systems and automated scientific instruments to accelerate discovery across medicine, materials, and energy.

What you will do

• Build domain models for perturbation biology, genetics, and multimodal experimental data.
• Translate biological questions into rigorous machine learning formulations and develop interpretable models grounded in biological mechanisms.
• Deliver calibrated posteriors, uncertainty estimates, and evaluation methods for predictions under unmeasured conditions.
• Partner with experimental scientists to shape data generation, model validation, and experiment-selection methods.
• Design benchmarks connecting model performance to biological and therapeutic outcomes, and use model outputs to prioritize targets and mechanisms.
• Integrate domain models into agentic workflows, deploy scientific tools, and help create environments for training reasoning models.

Requirements

• PhD in machine learning, statistics, computational biology, computer science, bioengineering, physics, or a related quantitative field, with a strong publication record or equivalent industry impact.
• Experience developing models for high-dimensional biological data and connecting machine learning methods to biological mechanisms and experimental design.
• Experience building models that generalize to sparse or unbalanced experimental designs, held-out combinations, or new contexts.
• Working knowledge of calibration, proper scoring rules, and rigorous evaluation design.
• Strong programming skills, reliable machine learning research workflows, and clear communication across technical and scientific teams.
• Experience leading ambiguous research problems from formulation through execution.

Nice to have

• Experience with Bayesian hierarchical models, simulation-based or likelihood-free inference, amortized posterior inference, state-space or ODE-based models, neural differential equations, or mechanism-informed machine learning.
• Background in pharmacokinetic/pharmacodynamic, systems-biology, or physical modeling.
• Experience with active learning, Bayesian optimal experimental design, closed-loop experimentation, or lab-in-the-loop systems.
• Experience deploying research models into scientific decision-making workflows.

Culture & Benefits

• Full-time U.S. employees receive medical, dental, and vision coverage.
• Benefits include employer-paid life and disability insurance, flexible time off, company-wide holidays, and paid parental leave.
• Educational assistance, commuter benefits, bike-share memberships for office-based employees, and a subsidized lunch program are available.
• Employees outside the U.S. receive regionally tailored benefits.
• The work environment emphasizes truth, trust, curiosity, grit, and velocity while operating with startup speed.