Member of the Technical Staff, Pretraining

Output Biosciences; New York (NY); On-site; USD 180,000 - 260,000; Full time

Benefits offered by this job

Competitive salary and equity

Excellent medical, dental, and vision

Job summary

Output Biosciences is building a biological reasoning foundation model. You will advance core architecture and training for a system that learns biological reasoning from diverse data modalities. The role spans research to training, designing architectures and objectives, and evaluating learned representations.

You will own pretraining end-to-end, including experiment design, distributed multi-GPU training, and scaling. The team values rigor, reproducibility, and impact on biology.

Qualifications

• PhD with 2+ years post-doctoral or industry research in representation learning and model pretraining.
• Alternatively, a Bachelor's or Master's with 5+ years hands-on research in representation learning and model pretraining.
• Strong publication record at top venues (NeurIPS, ICML, ICLR) with contributions to pretraining methods, self-supervised 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 experience training models on distributed multi-GPU infrastructure.
• Ability to own the full research-to-training pipeline: design methods, train and ship models.
• Produce production-quality code that is well-tested and maintainable, with version control and code review.

Responsibilities

• Push forward the architecture and training objectives of our foundation model, designed 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.

Skills

Python

PyTorch

Distributed training

Experiment design

Code quality

Education

PhD

Masters or Bachelor's

Tools

Git

Jupyter

Job description

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 advance the core architecture and training of Output's foundation model, the system that learns biological reasoning from data. This role spans the full arc from research to trained model: you design architectures, develop training objectives, run pretraining at scale, and evaluate what the model has learned.

• You will push forward the architecture and training objectives of our foundation model, designing approaches that are purpose-built for biological reasoning

• You will develop methods for the model to learn across multiple biological data modalities simultaneously, building unified representations of molecular biology

• You will extend the model's reasoning capabilities across biological phenomena, pushing what it can predict and understand about binding, molecular properties, and biological function

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

• You will design evaluation frameworks that measure whether the model has learned real biological reasoning, not just statistical patterns in training data

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 representation learning and model pretraining

• You have a 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

• You have hands-on experience pretraining large models on diverse, heterogeneous data, including designing training objectives and scaling training infrastructure

• 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 rather than intuition

Bonus Points

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

• You have experience pretraining models on molecular or biological data

• You have experience with multimodal learning or learning from heterogeneous data sources

• 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.

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