Machine Learning Research Engineer

Lead Allies Inc • San Francisco, CA

3d • Hybrid

About The Position

We’re partnering with a fast-growing AI biotech startup building generative AI systems for protein therapeutics. The team behind this company has driven major breakthroughs in AI-based protein design over the past several years, including experimentally validated AI-generated proteins and diffusion models for protein structure and sequence. They’ve also built one of the first fully AI-driven computational nanobody design platforms, with designs currently being tested in the lab.
The company’s mission is to create AI systems capable of designing protein therapeutics for the most challenging and high-value disease targets, with the long-term goal of transforming drug discovery and expanding the range of diseases that can be treated.
They’re looking for candidates excited to join a small, high-impact team and play an expanding role as the company grows.
The Role
Extend and scale an in-house deep generative modeling toolkit for downstream molecular design applications
Design and execute deep learning experiments to improve model performance and develop new functionality (e.g. loop engineering, protein–protein complex structure prediction)
Collaborate closely with software engineers to build systems for efficient training, evaluation, and deployment of deep learning models

Requirements

• Self-starter who enjoys tackling hard, open-ended scientific problems
• Strong critical thinking and experimental design skills
• Proficient in Python
• Hands-on experience with deep learning frameworks (e.g. PyTorch)
• 3+ years of industry experience in ML, data science, or engineering
• Demonstrated track record of impactful work in ML / deep learning (industry or academia)

Nice To Haves

• Graduate degree in math, CS, statistics, bioengineering, computational biology, or a related field (nice to have, not required)
• Background in physics, math, molecular biology, chemistry, or related fields
• Experience applying ML to structural biology or molecular design
• Strong publication record

Responsibilities

• Extend and scale an in-house deep generative modeling toolkit for downstream molecular design applications
• Design and execute deep learning experiments to improve model performance and develop new functionality (e.g. loop engineering, protein–protein complex structure prediction)
• Collaborate closely with software engineers to build systems for efficient training, evaluation, and deployment of deep learning models

Benefits

• Opportunity to work on cutting-edge generative AI with real-world biological impact
• High ownership and influence in a small, mission-driven team
• Collaboration with researchers and engineers from top institutions
• Work that directly contributes to the future of AI-driven drug discovery

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 11-50 employees