Senior DevOps Engineer

About us Encord is the universal data layer for AI that helps 300+ AI teams train and run models on the right data. Our platform indexes, curates, annotates, and evaluates data across the full AI lifecycle, from development through production.   Trusted by Woven by Toyota, AXA, UiPath, Zipline, and more. We're an ambitious team of 100+ working at the frontier of AI and have raised $60M in Series C funding from Wellington Management, CRV, Next47 and Y Combinator. The role We're looking for a Senior DevOps Engineer with 5+ years of experience to join our growing platform engineering team in New York. You'll be embedded in the teams building and operating Encord's core infrastructure, ensuring our platform is performant, reliable, observable, and scalable. You'll drive a culture of automation, performance, and resilience through individual contributions and collaboration with multiple squads. What You'll Do - CI/CD & Deployment Own and continuously improve deployment pipelines; partner closely with developers to review infrastructure changes, streamline release processes, and champion DevOps best practices across the engineering group. - Infrastructure & Cloud Design, deploy, and maintain cloud infrastructure on GCP and AWS; manage Kubernetes clusters, networking, and storage at petabyte scale, with infrastructure-as-code as the default. - Automation & Tooling Drive developer productivity by building, guiding, and reviewing automation and internal tooling efforts across the engineering group; eliminate manual toil wherever it exists. - Performance & Capacity Profile and optimise services handling large-scale data pipelines; perform capacity planning for storage and compute-intensive workloads. Work with squads to establish performance benchmarks and expectations. - Reliability & Availability Define and own SLIs/SLOs/SLAs for critical services; build alerting, runbooks, and incident response processes; lead postmortems with a blameless culture. - Observability Instrument services with distributed tracing, logging, and metrics (Prometheus, Grafana, OpenTelemetry, GCP Dashboards or similar); build infrastructure, define best practices, and work with each squad to ensure every service is observable before it goes to production. What We're Looking For - 4+ years of hands-on DevOps, platform engineering, or SRE experience in a production environment. - Strong experience building and maintaining CI/CD pipelines and deployment automation at scale. - Proven experience with infrastructure-as-code tools (e.g., Terraform, Pulumi) and configuration management. - Strong fundamentals in designing, building, and maintaining resilient distributed and/or high performance systems. - Hands-on experience with Kubernetes and containerised workloads in cloud environments (GCP and/or AWS). - Solid understanding of networking, operating systems, and database technologies. - Experience with observability fundamentals metrics, logs, traces, and alerting. Tech stack - We are technology agnostic at Encord and not looking for experience across all of these — as long as you're open to learning, please apply. - Backend: Python - Frontend: TypeScript and React - Deployment: Kubernetes - Infrastructure: GCP - Machine learning: PyTorch, CUDA, Ray What We Offer - Competitive salary, commission, and meaningful equity in a high-growth start-up - Clear, accelerated growth opportunities as the company scales rapidly - Strong in-person culture: 4 days/week - Flexible PTO to fully recharge - Annual learning & development budget - Comprehensive health, dental, and vision coverage - Frequent travel opportunities across the U.S., London, and Europe - Bi-annual company offsites, twice-weekly team lunches, and monthly socials Encord offers a unique opportunity to be part of a startup with a clear mission and vision. You will get to explore and build services enterprise AI use cases across many different industry verticals such as healthcare, surveillance, retail, agriculture, and many more.   Our work is at the cutting edge of computer vision and deep learning, which also includes working on solving unsolved problems within those fields.