Machine Learning Engineer, Pre-training

About Boltz

Boltz is a public benefit company building the next generation of AI-powered molecular modeling tools to make biology programmable and accelerate drug discovery, while keeping frontier capabilities broadly accessible.

Boltz began as an MIT spin-out. Our founders built Boltz-1 at MIT, and it became the first fully open-source model to match AlphaFold 3's accuracy in predicting the 3D structure of biomolecular complexes.

We focus on two things

• Open foundation models. Boltz-1, Boltz-2, and BoltzGen are open models trusted by 100,000+ scientists across biotech and academia, and used in programs at every Top 20 pharma as well as leading agrichemical and industrial research organizations.
• Boltz Lab. Our commercial platform runs our latest models and design agents as reliable, production-grade tools built around real chemistry and biology workflows. Teams start from a target and a hypothesis, then generate, evaluate, and rank candidate molecules, while we provide the compute, infrastructure, and collaboration layer so their scientists can move faster.

The two feed each other: open-source adoption earns scientific credibility and community insight, and Boltz Lab turns that into real impact on our partners' discovery programs.

You can read more about our mission, research and product vision in our manifesto.

About the role

As a Machine Learning Engineer on Pre-training, you will own the engineering behind our next open foundation model, Boltz-3: the data, training and evaluation systems that let us train it at scale and release it with state-of-the-art structure prediction. Every Boltz model starts here, and the open release is what 100,000+ scientists build on.

You will work closely with the Pre-training team's research scientists, Research Engineering, who own training speed and cluster tooling, our Post-training team, and Platform Engineering.

This role is ideal for an engineer who wants to build the systems behind a frontier open model and see it used across biotech and academia.

What you'll do

• Build and scale the data pipelines behind pre-training: ingesting, filtering, clustering and versioning large structural and sequence datasets.
• Own the pre-training codebase and large training runs with research scientists, keeping experiments reproducible and runs healthy.
• Build the evaluation and benchmark suites that show state-of-the-art antibody–antigen and small-molecule structure prediction.
• Build inference infrastructure for large sampling runs, to measure how performance scales with inference-time compute.
• Get each model ready for open-source release: packaging, documentation, inference code and community feedback that flows back to the team.

About you

Minimum Requirements

• Senior or Staff-level experience building ML systems for large-scale training, with strong Python and PyTorch.
• Experience with distributed training and data processing on multi-node GPU clusters.
• Solid software engineering fundamentals: clean, tested, maintainable code and reproducible experiments.
• A track record of shipping models or ML infrastructure that others relied on, such as a released model, a widely used codebase or a production system.
• Close collaboration with research scientists, and enough ML intuition to judge how engineering choices affect results.

Preferred Requirements

• Experience with structure prediction, protein or molecular data, or diffusion models.
• Experience maintaining open-source ML projects; early-stage startup experience.
• Experience using Boltz models, or open-source contributions.

We know great people don't always tick every box. If this role excites you and you meet most of the minimum requirements, we'd like to hear from you.

What we offer

• Build the systems behind open foundation models used by 100,000+ scientists.
• Work alongside one of the most talent-dense teams in the field.
• Significant ownership and independence, with responsibility for driving projects from concept to deployment.
• Highly competitive salary with meaningful equity in a public benefit company.
• Benefits:
• 25 days plus bank holidays a year
• Annual team offsite trip
• Private healthcare (BUPA), covering pre-existing conditions
• Pension program
• Paid parental leave
• Wellhub gym membership
• Free lunch every day
• Travel expenses

How we hire

1. Initial screen with a member of the Talent team
2. Technical interview
3. Onsite in London or virtual onsite with the team
4. Offer + Onboarding

Our commitment

Boltz is committed to building a diverse team and an inclusive workplace. We welcome applicants of all backgrounds. If you need any adjustments during the hiring process, tell us at [email] and we'll make it work.