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-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.

About the role

As an MLOps Engineer, you will focus on optimizing, deploying, and operating large-scale machine learning models that power Boltz Lab. Your primary responsibility will be to ensure that advanced models for molecular modeling and design run efficiently, reliably, and cost-effectively across distributed systems.

You will work closely with ML Researchers to take trained models and turn them into production-ready services by optimizing training and inference performance, reducing memory and compute overhead, and scaling workloads across multi-GPU and cloud environments. This includes profiling, improving model throughput and latency and hardening systems for long-running and high-volume workloads.

This role is ideal for someone who thrives on technical ownership and operational excellence, enjoys working close to systems and infrastructure, and is motivated by deploying high-impact machine learning systems at scale for real-world scientific use.

About you

Essentials:

5+ years of experience in industry
Strong experience deploying and operating machine learning models in production environments.
Proven ability to optimize training and inference workloads, including profiling performance, reducing memory and compute usage, and improving throughput and latency.
Hands-on experience with distributed frameworks and tooling
Hands-on experience with PyTorch and the scientific Python ecosystem.
Strong understanding of MLOps best practices, including experiment tracking, model versioning, reproducibility, and CI/CD for ML systems.
Strong software engineering fundamentals, with experience building reliable, well-tested, and maintainable ML infrastructure.
Comfortable collaborating closely with ML researchers to translate research models into robust production services.

Nice to have:

Exposure to computational biology or chemistry workflows and data formats.
Background working with large-scale scientific or numerical workloads.
Experience operating ML systems under real-world constraints such as cost, latency, and reliability.

What we offer

Opportunity to drive outsized real-world impact by building tools that empower thousands of scientists across the industry.
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 substantial equity ownership.