# Member of the Technical Staff, Generative at Output Biosciences

> Onsite · United States · Staff · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Aoutput%3Ae47dadff-8a9c-4749-8234-2db3f4b7fd57 (read 2026-11-06).

## Facts (observed)

- Company: [Output Biosciences](https://alderhq.app/companies/output-biosciences)
- Location text: New York, NY; San Francisco, CA
- Where it can be done from (inferred): Onsite · United States (evidence: "Location: New York, NY; San Francisco, CA")
- Posted: 2026-07-27 (102 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-03
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/output/e47dadff-8a9c-4749-8234-2db3f4b7fd57
- Skills listed (inferred): Python, PyTorch

## Pay and sponsorship

- Posted pay: $150k–$350k per year (from the listing's pay field)
- Market band for similar posted roles (New York, n 272): p25 $200k, median $234k, p75 $270k

## Description (the employer's text)

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 lead the design and development of Output's generative models, working across molecular modalities to build systems that produce novel, biologically grounded molecules. This role spans the full arc from research to trained model: you design architectures, develop training approaches, run experiments on distributed GPU clusters, and evaluate results.

You will design and build generative architectures for molecular data spanning multiple modalities, including small molecules, peptides, mini proteins and more

You will develop training approaches that learn from diverse biological signal, ensuring the model composes genuinely novel structures

You will build methods for controllable, targeted generation, enabling the model to produce molecules with specified biological properties while satisfying real-world chemical constraints

You will integrate biological reasoning from our foundation model into the generative pipeline, using learned biological representations to guide and condition generation

You will own training end-to-end: experiment design, distributed training on multi-GPU clusters, hyperparameter optimization, and iteration

You will design evaluation frameworks that go beyond statistical metrics to measure whether generated molecules are biologically meaningful, structurally valid, and genuinely novel

### About You

You have a PhD in computer science, machine learning, physics, mathematics, or a related field with 2+ years of post-doctoral or industry research experience, or a Bachelor's or Master's degree with 5+ years of hands-on research and engineering experience in generative modeling

You have a strong publication record in generative methods at top-tier venues (e.g., NeurIPS, ICML, ICLR)

You have extensive hands-on experience designing, building, and training deep generative models, including work on novel architectures, training objectives, or sampling methods

You are proficient in Python and PyTorch, and have experience training models on distributed multi-GPU infrastructure

You have demonstrated the ability to own the full research-to-training pipeline: you do not just design methods, you train and ship models

You write production-quality code that is well-tested and maintainable, and you are comfortable working in shared codebases with version control and code review

You are a rigorous experimentalist who designs evaluations carefully, tracks experiments systematically, and draws conclusions from data

### Bonus Points

You have experience applying generative models to molecular, chemical, or biological data

You have a background in chemistry, biology, computational biology, biophysics, or a related natural science

You have experience with multi-modal learning or cross-modality translation

You have experience with conditional or controllable generation methods

You have contributed to open-source machine learning projects

### 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.

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Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aoutput%3Ae47dadff-8a9c-4749-8234-2db3f4b7fd57 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.