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Platform Engineer at Helical
London, England, United Kingdom -
Full Time

Start Date

Immediate

Expiry Date

10 Jul, 26

Salary

0.0

Posted On

11 Apr, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Kubernetes, Docker, Python, Bash, AWS, GCP, Azure, CI/CD, Observability, Infrastructure as code, Databases, GPU workloads, ML infrastructure, Prometheus, Grafana

Industry

Biotechnology Research

Description
Helical is the AI-native lab for biology. We turn biological foundation models into production systems for discovery—so scientists can run experiments in silico at the speed of inference. We’re already deployed with top pharma, supporting work from target identification to biomarker discovery. The Role We’re hiring a Platform Engineer to build and scale the infrastructure behind our virtual AI lab. This is a hands-on role: debugging Kubernetes one moment, improving architecture the next, shipping to production throughout. You’ll be working on the system that makes AI-driven drug discovery actually usable at scale. What You’ll Do Run and scale Kubernetes (incl. GPU workloads) Own cloud infrastructure and infra-as-code Support ML training & inference pipelines Manage CI/CD, observability, and deployments Handle databases, storage, and migrations Build automation (Python/Bash) Work closely with ML, backend, and product What We’re Looking For 3+ years in platform / DevOps / infra Strong Kubernetes, Docker, cloud (AWS/GCP/Azure) Solid Python Experience with CI/CD, databases, infra-as-code Comfortable debugging production systems High ownership, low ego Nice to Have GPU workloads / ML infra Observability tools (Prometheus, Grafana, etc.) Security/compliance (SOC2, HIPAA) Biotech / pharma experience Why Helical Live with top pharma High ownership, small team Work at the intersection of AI, biology, and systems Build something that actually changes how medicine is made
Responsibilities
The Platform Engineer will build and scale the infrastructure for an AI-driven virtual lab, including managing Kubernetes, GPU workloads, and CI/CD pipelines. They will collaborate closely with ML, backend, and product teams to ensure production systems are robust and scalable.