⚡DevOps Engineer

🌍Location: San Francisco, CA (On-site)

💲Compensation: $160,000-$225,000 base + strong upside

My client is an early-stage, well-backed deep tech company, building an end-to-end platform spanning advanced modelling, environmental sensing, large-scale simulation, and real-world engineering systems, aiming to convert cutting-edge research into deployable, operational capability.

The team brings together expertise across machine learning, atmospheric science, high-performance computing, aerospace systems, and software engineering. Despite being a lean, high-calibre team, they are operating at a level of ambition typically associated with national labs and large-scale research programmes.

The Role

We’re looking for a DevOps Engineer to own the infrastructure layer underpinning the entire platform. You’ll be responsible for building and scaling the cloud, compute, and deployment systems that enable a deeply technical team to operate with the velocity and leverage of a much larger organisation.

What You’ll Do

Own and scale multi-cloud infrastructure across AWS and GCP
Build and maintain Terraform-based infrastructure across environments
Design and operate robust CI/CD pipelines
Manage containerised systems (Docker, Kubernetes)
Support HPC and GPU compute environments for simulation and ML workloads
Implement monitoring, observability, and reliability best practices
Drive security, cost optimisation, and infrastructure governance

What We’re Looking For

Strong experience with AWS and/or GCP
Deep Terraform / infrastructure-as-code expertise
Experience designing and operating CI/CD systems
Solid understanding of containers (Docker, Kubernetes)
Strong Linux, networking, and production systems experience
Comfortable operating in a fast-moving, high-ownership environment

Nice to Have

Experience with HPC / GPU infrastructure (SLURM, ParallelCluster)
Background in ML, simulation, or data-intensive systems
Workflow orchestration tools (Airflow, Dagster, Prefect)

Why This Role

Work on problems rarely tackled outside government or elite research environments
Own infrastructure at a foundational level in a small, high-calibre team
Operate at the intersection of science, ML, and real-world deployment
Massive scope across cloud, compute, and large-scale data systems

Build the infrastructure powering systems designed to solve some of the most complex real-world challenges at global scale.

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