Member of the Technical Staff, Biological Data

Responsibilities

• Construct training datasets that capture protein and molecule interactions from diverse biological sources
• Develop methods to expand training data beyond public databases using biological and chemical reasoning
• Design benchmarks grounded in real molecular phenomena to measure model capabilities
• Collaborate with model researchers to develop data strategies and prioritize biological signals
• Design approaches for integrating data across biological scales and modalities
• Design rigorous splitting and evaluation strategies to prevent data leakage
• Identify new biological data sources, experimental methods, and molecular databases

Requirements

• PhD in computational biology, biophysics, structural biology, chemistry, biochemistry, or related field
• 2+ years of post-doctoral or industry research experience
• Deep understanding of molecular interactions and protein structure
• Experience working with large-scale biological or molecular datasets
• Strong programming skills in Python
• Understanding of machine learning training data requirements (coverage, quality, balance)

Preferred Qualifications

• Experience with structure prediction, molecular docking, or virtual screening tools
• Experience training or evaluating machine learning models on molecular data
• Publications in computational biology, bioinformatics, or molecular informatics
• Background in cheminformatics or molecular data analysis
• Experience with protein or molecular language models

About the Company

Output is a stealth-stage startup building a biological reasoning model that understands biology at the scale and complexity of life. Backed by Y Combinator and top-tier VCs, the team consists of repeat founders and biotech veterans focused on generating novel therapies through AI.