Biological AI Foundation Model Engineer

Output Biosciences; San Francisco (CA); On-site; USD 150,000 - 230,000; Full time

Benefits offered by this job

Competitive salary & equity

Excellent medical, dental, and vision

Job summary

Output Biosciences in San Francisco is advancing its foundation model for biological reasoning, covering architecture design, training objectives, and large-scale pretraining across heterogeneous biological data. You will build unified representations that capture molecular interactions, properties, and biological function.

We seek researchers who can own the full pipeline from research to deployment, publishing in top venues, and delivering production-grade code within a collaborative,

Qualifications

• PhD in computer science, ML, physics, mathematics, or related field with 2+ years post-doc or industry research experience, or Bachelor's/Master's with 5+ years hands-on research in representation learning and model pretraining.
• Strong publication record at top-tier venues (NeurIPS, ICML, ICLR) with contributions to pretraining methods, self-supervised learning, or foundation models.
• Hands-on experience pretraining large models on diverse data, designing training objectives, and scaling training infrastructure.
• Proficient in Python and PyTorch, with experience training models on distributed multi-GPU infrastructure.
• Owns the full research-to-training pipeline: design methods, train and ship models.
• Writes production-quality code, well-tested, maintainable, familiar with version control and code review.
• Rigorous experimentalist who designs evaluations, tracks experiments, and draws data-driven conclusions.

Responsibilities

• Push forward the architecture and training objectives of our foundation model for biological reasoning.
• Develop methods for learning across multiple biological data modalities, building unified representations.
• Extend the model's reasoning capabilities across biological phenomena and predictive tasks.
• Own pretraining end-to-end: experiment design, distributed training, hyperparameter optimization, iteration.
• Design evaluation frameworks to measure genuine biological reasoning beyond statistical patterns.

Skills

Python

PyTorch

Distributed training

Research & publication

Git

Education

PhD in Computer Science / ML / Physics / Math

Bachelor's or Master's with 5+ years of research/engineering

Tools

Git

CUDA

Job description

Output Biosciences in San Francisco is advancing its foundation model for biological reasoning, covering architecture design, training objectives, and large-scale pretraining across heterogeneous biological data. You will build unified representations that capture molecular interactions, properties, and biological function.

We seek researchers who can own the full pipeline from research to deployment, publishing in top venues, and delivering production-grade code within a collaborative,