Head of Biology

Output Biosciences•New York, NY

1d•$120,000 - $250,000

About The Position

Output has built a biological reasoning model that understands biology at the scale and complexity life actually operates. Our model independently learned the principles of molecular interactions, opening up drug treatments that were previously impossible. We're already generating therapies that traditional approaches cannot reach. The hardest problems in both AI and biology are being solved here, and there is room for you to own one. Output is currently in stealth, operated by a team of repeat founders and biotech veterans with multiple exits in AI x Bio, and backed by top-tier VCs including Y Combinator. You own the feedback loop between Output's models and the lab. You go deep into specific biological problems, translate what the biology requires into priorities for data and modeling, and manage through CRO partners the experimental cycle that validates model outputs and generates new data. You will own the active learning cycle between Output's models and experimental validation: designing experiments, managing CRO partners for synthesis and testing, interpreting results, and feeding them back to improve the models You will dive into specific biological problem areas, developing the expertise needed to translate nuanced biological requirements into priorities for data construction, modeling, and evaluation You will manage CRO relationships for peptide and molecular synthesis, biological assays, and experimental testing, designing each campaign to generate maximally informative data You will collaborate with the engineering team to design and build biology-specific agent skills and workflows, encoding your biological expertise into tools that automate experimental decision-making You will work closely with the model and data teams, identifying biological phenomena the models should be sensitive to and ensuring that experimental findings shape what and how the models learn You will stay current with biological literature and experimental methods, continuously identifying new biological problems and translating them into opportunities for model development

Requirements

PhD in computational biology, biophysics, structural biology, molecular biology, or a related biological field with 3+ years of post-doctoral or industry research experience
Deep understanding of protein biology, protein-ligand interactions, and how sequence and structure relate to biological function
Experience designing biological experiments and working with CROs for synthesis and experimental validation
Experience working with protein language models or machine learning applied to biological sequences and structures
Strong programming skills in Python, with experience building computational pipelines and analysis tools
Ability to move between the language of biology and the language of machine learning: understanding what models need from data and what experimental results mean for model development
Comfortable going deep into unfamiliar biological problem areas and rapidly developing the expertise needed to guide modeling and experimental decisions

Nice To Haves

Structural biology expertise, including protein structure analysis and structure-function relationships
Experience with active learning or iterative experimental design in a computational biology or drug discovery setting
Hands-on wet lab experience
Experience across multiple therapeutic modalities, including peptides
Experience building tools or writing code that automates biological analysis workflows

Responsibilities

Own the active learning cycle between Output's models and experimental validation: designing experiments, managing CRO partners for synthesis and testing, interpreting results, and feeding them back to improve the models
Dive into specific biological problem areas, developing the expertise needed to translate nuanced biological requirements into priorities for data construction, modeling, and evaluation
Manage CRO relationships for peptide and molecular synthesis, biological assays, and experimental testing, designing each campaign to generate maximally informative data
Collaborate with the engineering team to design and build biology-specific agent skills and workflows, encoding your biological expertise into tools that automate experimental decision-making
Work closely with the model and data teams, identifying biological phenomena the models should be sensitive to and ensuring that experimental findings shape what and how the models learn
Stay current with biological literature and experimental methods, continuously identifying new biological problems and translating them into opportunities for model development