Member of Technical Staff, Biological Data

Output Biosciences; San Francisco (CA); On-site; USD 140,000 - 220,000; Full time

Job summary

Output Biosciences is seeking a PhD-level computational biologist to own the data that our models learn from, moving beyond surface patterns to genuine molecular understanding. You will curate, source and transform diverse biological datasets, grounding them in first-principles biology to improve model reasoning.

In this role you will design benchmarks, create new training signals where data is sparse, and collaborate with model researchers to align data with learning objectives across scales

Qualifications

• PhD in computational biology, biophysics, structural biology, chemistry or related field with 2+ years of post-doctoral or industry research experience, or equivalent depth through combined biology and computational background.
• Deep understanding of molecular interactions, protein structure, and biological data at the molecular level, grounded in first principles.
• Experience working with large-scale biological or molecular datasets, including sourcing, cleaning, integrating, and analyzing heterogeneous data.
• Strong programming skills in Python and building computational pipelines for data processing at scale.
• Understanding of what ML models require from training data: coverage, quality, balance and evaluation rigor.
• Data construction as a research problem, considering signal, meaning and gaps.

Responsibilities

• Construct training datasets capturing protein and molecule interactions from diverse sources.
• Develop methods to expand training data beyond public databases where data is sparse or absent.
• Design benchmarks grounded in real molecular phenomena to test model learning.
• Collaborate with model researchers to determine data needs and learning priorities across modalities.
• Integrate data across scales and modalities to create coherent training signals.
• Design robust data splitting/evaluation to ensure generalization to real biological problems.
• Keep current with biological data sources, experimental methods, and molecular databases.

Skills

Computational biology

Biophysics knowledge

Python programming

Large-scale data analysis

Data integration

ML data requirements

Molecular interactions understanding

Education

PhD in computational biology or related field

Job description

The Role

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 will own the data that our models learn from. This role requires a deep understanding of molecular biology - what a biological data source contains, what it implies, and what is missing. The quality and coverage of training data determines what our models can learn, and the biological insight behind how that data is constructed is the difference between a model that memorizes and one that reasons.

• You will construct training datasets that capture how proteins and molecules interact, drawing from diverse biological data sources and extending them with your understanding of molecular principles

• You will develop methods to expand training data beyond what exists in public databases, using biological and chemical reasoning to create new training signal where current data is sparse or absent

• You will design benchmarks grounded in real molecular phenomena, measuring whether our models have learned biologically meaningful capabilities rather than statistical shortcuts

• You will develop data strategies in collaboration with model researchers, determining what the model should learn from, what biological signal to prioritize, and how to sequence learning across modalities

• You will design approaches for integrating data across biological scales and modalities, building coherent training data from heterogeneous experimental and computational sources

• You will design rigorous splitting and evaluation strategies that prevent leakage and ensure model capabilities generalize to real biological problems

• You will stay current with biological data sources, experimental methods, and molecular databases, continuously identifying new sources of training signal

About You

• You have a PhD in computational biology, biophysics, structural biology, chemistry, biochemistry, or a related biological field with 2+ years of post-doctoral or industry research experience, or equivalent depth through a combined biology and computational background

• You have deep understanding of molecular interactions, protein structure, and biological data at the molecular level, grounded in first principles rather than surface familiarity

• You have experience working with large-scale biological or molecular datasets, including sourcing, cleaning, integrating, and analyzing heterogeneous data

• You have strong programming skills in Python and are comfortable building computational pipelines for data processing at scale

• You understand what machine learning models require from training data: coverage, quality, balance, and evaluation rigor

• You approach data construction as a research problem, not a pipeline task: you think carefully about what data means, what signal it carries, and what is absent

Bonus Points

• You have experience with computational biology tools such as structure prediction, molecular docking, or virtual screening

• You have experience training or evaluating machine learning models, particularly on molecular or biological data

• You have publications in computational biology, bioinformatics, or molecular informatics

• You have a background in cheminformatics or molecular data analysis

• You have experience working with protein or molecular language models

Our Values

Heart: We foster a culture of ownership. We are assembling a team of individuals who are passionate and take pride in their contributions.

Excellence: We have an unwavering commitment to excellence and continuously challenge ourselves to reach the highest standards.

Practicality: We value practicality and results-oriented thinking. We are committed to making a tangible impact on the lives of patients and the broader community.

Honesty: We place a high value on honesty and directness. We firmly believe in addressing issues as they arise, in an open and transparent manner.

Fun: We believe that life is too short to not have fun. Our goal is to create a workplace that is fun, engaging, rewarding and fulfilling.

What We Offer

• We encourage new and different ideas, creativity and contrarian thinking

• Healthy feedback focused environment to help you strive - leadership will have high expectations, regularly share constructive feedback, support you and help you grow, and welcome receiving feedback and ideas from you

• You own your day-to-day management. What we care about is that we all hit our milestones

• Competitive salary and equity in a growing, well-funded startup

• Excellent medical, dental, and vision coverage