DevOps Engineer, Encord
$180-230k
Offers Bonus & Equity
4+ days a week in office
Data platform for AI training
Job no longer available
Data platform for AI training
201-500 employees
Job no longer available
$180-230k
Offers Bonus & Equity
4+ days a week in office
201-500 employees
Company mission
To solve AI alignment by tackling the real bottleneck: data quality.
Role
Who you are
- We're looking for a DevOps Engineer with 4+ years of experience to join our growing platform engineering team in New York
- 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
- 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 the job involves
- 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
- 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
Our take
It has become increasingly clear that the next generation of AI applications will be driven by data-centric approaches concentrated around training data creation, management, and evaluation. Crucial within this process is the annotation or 'labelling' of data. Traditionally, such annotation has been left to human workers, which has historically made this part of the process of AI development expensive, slow, and difficult to scale.
Encord offers a computer vision annotation platform that automates a number of manual labelling tasks. Its suite of tools is designed for collaboration across roles and teams, from domain-expert annotators to project managers and machine learning engineers. This allows companies to iterate 10x faster and develop and deploy high-performing models in less time whilst saving on annotation costs.
Demand is clearly there: since its launch in 2020, Encord has facilitated the annotation of more than 250 million frames and images and served customers in verticals such as medical imaging, smart cities, sports analytics and satellite imaging. It was awarded $44.3 million in Series C funding in 2026, which will help it scale and meet demand for its AI-native data infrastructure. Indeed, usage is scaling rapidly, with the number of labels generated on the platform growing ~70% month-over-month.
Steph
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 5 rounds)
Feb 2026
$60m
SERIES C
Aug 2024
$30m
SERIES B
Total funding: $107.2m
Company benefits
- Competitive salary, commission, and meaningful equity in a high-growth startup
- Strong in-person culture: 3–5 days/week in our London, SF, and NYC offices
- Flexible PTO to fully recharge
- Annual learning & development budget
- Frequent travel opportunities across the U.S., London, and Europe
- Bi-annual company offsites, twice-weekly team lunches, and monthly socials
- Company lunches twice a week
- Clear, accelerated growth opportunities as the company scales rapidly
- Comprehensive health, dental, and vision coverage
Company HQ
North Beach, San Francisco
Articles
Leadership
Eric Landau
(Co-Founder & Co-CEO)With MScs from Harvard and Stanford, Eric spent 8+ years as a senior quant at DRW deploying ML at scale—before co-founding Encord to make data the core of AI development.
Ulrik Stig Hansen
(Co-Founder & Co-CEO)Ulrik spent 3+ years in Sales & Trading at J.P. Morgan and holds science master’s degrees from UCL and Imperial—before co-founding Encord to transform how AI handles data.
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