Infrastructure Engineer for ML Backend — Remote
Boltz
14 days+
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Job summary
Boltz is building AI-powered molecular modeling tools to accelerate drug discovery and make frontier chemistry accessible. As a Software Engineer in Infrastructure, you will design and operate core systems powering Boltz Lab, enabling large-scale modeling and design workflows for scientists worldwide.
You will implement reliable backend components, scale APIs and inference workloads, and collaborate with ML researchers, product engineers, and scientists to translate requirements into robust,
Qualifications
- Strong experience building backend or infrastructure systems in production environments.
- Solid software engineering fundamentals with reliable, maintainable, and well-tested code.
- Experience designing and scaling APIs and services used by external users or internal teams.
Responsibilities
- Design, implement, and maintain reliable, scalable backend and infrastructure components that support ML inference, data pipelines, and product features.
- Build APIs, scale inference workloads, and evolve data ingestion and storage pipelines.
Skills
Backend systems
API design
Collaboration
Ownership
Tools
Python
TypeScript
PostgreSQL
AWS
Docker
Kubernetes
Job description
View all jobs Software Engineer, Infrastructure London • Remote Engineering Remote • In office Full-time
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 a Software Engineer in Infrastructure, you will build and operate the core systems that power Boltz Lab and enable scientists worldwide to run large-scale molecular modeling and design workflows. Your primary responsibility will be to design, implement, and maintain reliable, scalable backend and infrastructure components that support ML inference, data pipelines, and product features. You will work closely with ML researchers, product engineers, and scientists to turn cutting-edge models into dependable, production-grade services. This includes building APIs, scaling inference workloads, evolving data ingestion and storage pipelines, and ensuring the platform meets real-world requirements around performance, reliability, and cost. This role is ideal for someone who enjoys owning foundational systems end-to-end, thrives on ambiguity, and is motivated by building infrastructure that directly impacts real scientific and experimental outcomes.About you
Essentials:
- Strong experience building and operating backend or infrastructure systems in production environments.
- Solid software engineering fundamentals, with a track record of designing reliable, maintainable, and well-tested systems.
- Experience designing and scaling APIs and services used by external users or internal teams.
- Proficiency with modern backend tooling, such as Python and/or TypeScript, and experience with relational databases (e.g. Postgres).
- Hands-on experience with cloud and containerized environments (e.g. AWS, Docker, Kubernetes).
- Comfortable collaborating across engineering, research, and product to translate requirements into robust systems.
- Willingness to take ownership of critical infrastructure and make pragmatic trade-offs under real-world constraints.
Nice to have:
- Experience supporting or integrating machine learning systems (e.g. inference services, batch pipelines, or training workflows).
- Exposure to scientific computing, computational biology, or chemistry workflows.
- Experience designing data pipelines for large, complex, or high-throughput datasets.
- Familiarity with operating systems under constraints such as latency, cost, uptime, and rapid iteration.
- Prior experience working in an early-stage or fast-moving startup environment.
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.
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