Applied ML Engineer/Scientist

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-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.

We deliver these capabilities through Boltz Lab, our platform for running our latest models and design agents as reliable, production-grade tools. Boltz Lab is designed around real chemistry and biology workflows, so teams can start from a target and a hypothesis and quickly generate, evaluate, and rank candidate molecules. We provide the compute, the scalable infrastructure, and the collaboration layer, so scientists can iterate faster and stay focused.

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

As an Applied ML Engineer/Scientist, you will apply and adapt Boltz’s foundational machine learning models to real-world drug discovery problems, working directly on projects with external partners. Your focus will be on finding the most effective ways to fine-tune, condition, and deploy state-of-the-art models for specific scientific use cases in molecular modeling and design.

You’ll collaborate closely with ML researchers, software engineers, and domain experts in chemistry and biology to translate partner needs into concrete modeling strategies. This includes curating and adapting datasets, selecting and tuning model architectures and objectives, and iterating rapidly to maximize performance on applied tasks. You’ll own the end-to-end applied modeling loop from problem formulation and experimentation to evaluation and delivery.

You’ll also feed applied learnings back into core model development, identifying gaps and failure modes in practice and contributing concrete improvements that strengthen Boltz’s foundational models.

This role is ideal for someone who enjoys operating at the interface between cutting-edge ML and real-world deployment: a technically strong, execution-focused scientist or engineer who wants to turn powerful foundational models into reliable, high-impact capabilities for partners and end users.