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# Software Engineer, Infrastructure

- **Company:** Boltz
- **Location:** Greater London, UK
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Cloud Computing, Computational Biology, Relational Databases, Python (Programming Language), PostgreSQL, Uptime, Molecular Modeling, Scientific Computating, TypeScript, Web Services, Data Ingestion, Backend, Containerization, Kubernetes, Low Latency, Machine Learning Operations, Api Design, Data Pipelines, Docker
- **Published:** September 9, 2026
- **Apply:** https://www.collegerecruiter.com/job/2858068345-software-engineer-infrastructure

## About the Role

* 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., * 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.

## Description

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.


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