Biotech Foundation ML Engineer: Biology & AI

Output Biosciences; San Francisco (CA); On-site; USD 180,000 - 280,000; Full time

Benefits offered by this job

Idea-friendly environment

Constructive feedback culture

Ownership of milestones

Competitive salary and equity

Medical, dental, and vision coverage

Job summary

Output Biosciences is building a biological reasoning model that understands biology at life’s scale and complexity. We’re creating therapies beyond traditional approaches, and you will own the core architecture and training of our foundation model spanning research to a trained system.

You will push forward architecture, pretraining objectives, and evaluation frameworks, enabling multi-modal learning across biological data modalities and rigorous scientific validation in a collaborative startup

Qualifications

• PhD or equivalent with strong research track record in ML and representation learning.
• Proven ability to design, train and evaluate large pretraining models.
• Expertise in Python and PyTorch; experience with distributed multi-GPU infra.
• Ability to own end-to-end research-to-training pipeline with production-quality code.
• Proven publication record at top venues and ability to work in a team.

Responsibilities

• Advance architecture and training of the foundation model for biological reasoning.
• Design training objectives and run pretraining at scale on multi-GPU clusters.
• Develop evaluation frameworks to measure genuine biological reasoning.
• Own end-to-end research-to-training workflow from experiments to shipped models.
• Collaborate with a team of biotech and AI veterans to push state-of-the-art.

Skills

Python

PyTorch

Distributed training

Experimentation & evaluation

Research-to-training pipeline

Version control

Education

PhD in CS/ML/Physics/Math

Bachelor's/Master with 5+ years in related field

Tools

Git

High-performance computing

Job description

Output Biosciences is building a biological reasoning model that understands biology at life’s scale and complexity. We’re creating therapies beyond traditional approaches, and you will own the core architecture and training of our foundation model spanning research to a trained system.

You will push forward architecture, pretraining objectives, and evaluation frameworks, enabling multi-modal learning across biological data modalities and rigorous scientific validation in a collaborative startup