Staff DevOps Engineer, Software, Product Operations

External

Lilasciences · Cambridge, UK

$192K–$272K/yrFull-timeOn-site1mo ago

AWSCapacity PlanningChaos EngineeringCI/CDGCPGitHub

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Benefits

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.Expected Base Salary Range$192,000 - $272,000 USDAbout LILALila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.We're All InLila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender ideDental insuranceVision insuranceFlexible scheduleEquity / stock optionsPerformance bonusParental leave

Additional Information

Your Impact at LILA The Staff/Principal DevOps Engineer will drive the design, implementation, and optimization of our infrastructure and delivery platforms. This role bridges platform engineering, site reliability, and DevOps practices, building scalable, automated systems that enable fast, reliable software delivery across cloud and Kubernetes environments. You will collaborate with software engineers, lab scientists, and ML engineers to build infrastructure that powers automated scientific analysis, experiment orchestration, and more. What You'll Be Building Build Kubernetes-based systems supporting scientific services, ML pipelines, and platform workloads; including production hardening, RBAC, network policies, and Pod Security Standards CI/CD pipelines with GitHub Actions/GitLab CI implementing best practices: build attestations, SBOM generation, dependency scanning, and container image hardening Infrastructure-as-code with Terraform and Helm; policy-as-code guardrails (OPA/Kyverno/Checkov) with drift detection AWS cloud infrastructure: EKS clusters, IAM least privilege, VPC/PrivateLink networking, KMS/Secrets Manager, ECR, S3, and centralized logging/monitoring Platform tooling to streamline deployment, observability, and developer workflows, enabling self-service with secure defaults Reliability engineering: SLOs/SLIs, incident response, capacity planning, and performance optimization throughout the stack Software supply chain practices: artifact signing, registry governance and vulnerability management QA and testing infrastructure: static analysis and code quality gate enforcement in CI pipelines, automated end-to-end and browser-based regression test suites, ephemeral test environments for PR-based validation, and pre-merge quality checks Automation and tooling in Python or Go to improve infrastructure operations and integrate telemetry with observability platforms What You'll Need to Succeed Expertise in DevOps, SRE, Systems Engineering, or Platform Engineering in large scale cloud environments Expertise in deploying to cloud environments (AWS, GCP, etc) using infrastructure-as-code (Terraform, Helm) and containerization Deep experience with CI/CD systems (GitHub Actions, GitLab CI, or Jenkins) and GitOps practices Strong proficiency in Python/scripting languages for automation and tooling Strong understanding of Kubernetes operations: deployments, networking, storage, observability, and troubleshooting Bonus Points For SRE practices: observability platforms, chaos engineering, incident management Securing ML/AI pipelines (model registries, training clusters, inference gateways) Experience in regulated/audit-heavy environments (SOC 2, ISO 27001) Supply chain security maturity: SBOMs, image signing, SLSA concepts Administering static analysis platforms (custom quality profiles, security hotspot triage) and scaling browser-based test suites across parallel CI environments Prior startup/high-growth experience balancing velocity with reliability

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