AI/ML Scientist, Computational Biology & Genomics

Responsibilities

• Design, train, and evaluate machine learning models (classical, deep learning, and hybrid) on large-scale NGS datasets.
• Develop novel modeling approaches for extracting disease-relevant signals from high-dimensional biological data.
• Collaborate closely with computational and experimental scientists to ensure models reflect biological reality and inform assay design.
• Translate model outputs into actionable insights that guide experimental and clinical decision-making.
• Contribute to publications and present work at leading AI/ML venues (e.g., NeurIPS, ICML, ICLR, AAAI, and similar).
• Write clean, reproducible, and well-tested code following best practices in scientific computing.
• Promote a culture of scientific integrity, growth, transparency, collaboration, mutual respect, and fun, while contributing to our goal of improving patient lives.

Qualifications

• PhD in Computer Science, Statistics, Computational Biology, or a related quantitative field (or Master's with equivalent research experience).
• Track record of first-author or significant contributions to publications applying machine learning to biological or biomedical data.
• Strong hands-on experience with PyTorch and the scientific Python ecosystem (NumPy, SciPy, Pandas, etc.).
• Demonstrated experience designing, training, and rigorously evaluating deep learning models (e.g., ablation studies, failure analysis, and interpretability studies).
• Strong intuition for modeling tradeoffs, including when to apply simpler vs. more complex methods.
• Experience with or knowledge of ML infrastructure engineering best practices, including GPU-based training workflows.
• Experience with cloud environments (e.g., GCP, AWS), high-performance computing, and version control (Git/Github).
• Able to effectively perform scientific literature reviews that drive insights.
• Experience contributing to and maintaining deep-learning codebases, with a high bar for engineering quality, reproducibility, and testing.
• Able to work from our San Mateo office at least 2-3 days per week.