Member of the Technical Staff, Pretraining

Output Biosciences•New York, NY

4d•$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.

Requirements

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 representation learning and model pretraining.
Strong publication record at top-tier venues (e.g., NeurIPS, ICML, ICLR) with contributions to pretraining methods, self-supervised learning, representation learning, or foundation models.
Hands-on experience pretraining large models on diverse, heterogeneous data, including designing training objectives and scaling training infrastructure.
Proficient in Python and PyTorch, and have experience training models on distributed multi-GPU infrastructure.
Demonstrated ability to own the full research-to-training pipeline: you do not just design methods, you train and ship models.
Write production-quality code that is well-tested and maintainable, and you are comfortable working in shared codebases with version control and code review.
Rigorous experimentalist who designs evaluations carefully, tracks experiments systematically, and draws conclusions from data rather than intuition.

Nice To Haves

Background in chemistry, biology, computational biology, biophysics, or a related natural science.
Experience pretraining models on molecular or biological data.
Experience with multimodal learning or learning from heterogeneous data sources.
Contributed to open-source machine learning projects.

Responsibilities

Advance the core architecture and training of Output's foundation model, the system that learns biological reasoning from data.
Push forward the architecture and training objectives of our foundation model, designing approaches that are purpose-built for biological reasoning.
Develop methods for the model to learn across multiple biological data modalities simultaneously, building unified representations of molecular biology.
Extend the model's reasoning capabilities across biological phenomena, pushing what it can predict and understand about binding, molecular properties, and biological function.
Own pretraining end-to-end: experiment design, distributed training on multi-GPU clusters, hyperparameter optimization, and iteration.
Design evaluation frameworks that measure whether the model has learned real biological reasoning, not just statistical patterns in training data.