Member of the Technical Staff, Generative

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; 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; 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