Member of Technical Staff: Computational Biology

Radical Numerics • San Francisco, CA

3d

About The Position

Radical Numerics is an AI research lab building general biological intelligence. The Member of Technical Staff: Computational Biology brings deep biological expertise to the data, tasks, and analyses behind the lab’s models. The role turns questions in genomics, gene regulation, protein biology, or systems biology into well-defined computational work and interprets results with sound scientific judgment.

The position can be shaped around a specialty such as data curation, bioinformatics pipelines, or model evaluation. The role partners with model researchers and experimental collaborators to build datasets and analyses that support scientific work.

Requirements

• Exceptional research record in computational biology, bioinformatics, genomics, or another relevant area of biology, demonstrated through a PhD or equivalent research or industry experience.
• Deep expertise in at least one biological domain, with strong scientific judgment and careful attention to data quality and experimental details.
• Strong Python or R programming skills and experience building reliable, reproducible scientific analyses or software.
• Ability to work across biology, machine learning, and engineering, and communicate assumptions, results, and limitations clearly.
• Experience working with agentic coding tools.

Nice To Haves

• Experience with large-scale biological data curation, data mixing, or bioinformatics infrastructure.
• Experience with biological foundation models or research contributions, datasets, methods, or software used by other scientists.

Responsibilities

• Curate and integrate large biological datasets, maintaining clear provenance, quality control, and documented assumptions.
• Build reproducible bioinformatics and analysis pipelines across relevant data types, such as sequencing, functional genomics, single-cell, perturbational screening, or protein data.
• Help design data mixtures and sampling strategies for model training; investigate how dataset composition, assay effects, duplication, and biological coverage affect downstream results.
• Define biologically meaningful tasks, endpoints, and baselines with modeling and evaluation teams.
• Investigate model predictions and failure modes in the context of available biological evidence.
• Work with wet-lab collaborators to formulate testable hypotheses, prioritize validation experiments, and generate new training datasets.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree