Staff Engineer, CI​/CD & Cloud Infrastructure

Position Overview

We are looking for a Staff CI/CD & Cloud Infrastructure Engineer to own and evolve our build pipelines, deployment workflows, and cloud infrastructure. You will be responsible for ensuring that software — spanning Python, C/C++, and CUDA on Linux — is built, tested, versioned, and deployed reliably across both AWS cloud environments and a fleet of complex embedded instruments operated in our central lab facility.

Location: San Diego, CA
Job Type: Full‑Time
Compensation Range: $175K - $185K

• Design, build, and maintain CI/CD pipelines using Git Hub Actions or similar platforms
• Manage build systems for Python, C/C++, and CUDA codebases on Linux
• Integrate build tools (CMake, Make, pip, setup tools) into automated pipelines
• Implement robust versioning, tagging, and artifact management strategies
• Ensure full traceability of builds, test results, and artifacts from commit to deployment
• Manage Docker-based build environments including base images, caching, and reproducibility
• Maintain and optimize build performance, parallelism, and reliability

• Architect and manage complex AWS infrastructure including:
  • IAM roles, policies, and access management
  • Storage services (S3, EBS, EFS) with tiered lifecycle policies
  • Databases (RDS, DynamoDB, or similar) with backup and
  • Data workflow and pipeline engines (Step Functions, Airflow, or
  • Compute services (EC2, ECS, EKS, Lambda) scaled to workload
• Implement infrastructure as code using Terraform
• Manage Kubernetes clusters and Helm charts for containerized workloads
• Design for scalability, high availability, and disaster recovery
• Manage cost optimization, resource tagging, and infrastructure governance
• Support multi‑account and multi‑region strategies as needed
• Familiarity with Azure and GCP for secondary or hybrid requirements

• Provision, configure, and manage on‑premises Linux HPC nodes used for secondary and tertiary data processing
• Define infrastructure‑as‑code (Terraform, Ansible, or similar) for reproducible HPC node provisioning and configuration
• Manage high‑speed networking infrastructure between instruments, HPC nodes, and storage (configuration, monitoring, troubleshooting)
• Implement and manage shared storage systems (NFS, parallel file systems, or similar) accessible to both local HPC and cloud compute
• Design and operate hybrid burst‑to‑cloud infrastructure — provision and manage AWS compute resources that extend local HPC capacity on demand
• Collaborate with the data pipeline team to ensure infrastructure meets throughput, latency, and reliability requirements
• Manage OS patching, driver updates, and GPU runtime environments across HPC nodes
• Monitor HPC cluster health, utilization, and capacity to inform scaling decisions

• Design and operate data ingestion pipelines for high‑volume experiment data from lab instruments
• Implement tiered storage strategies (hot/warm/cold) to balance accessibility, performance, and cost
• Deploy and manage search infrastructure (Elasticsearch/ Open Search) to make experiment data universally discoverable and queryable
• Build data cataloging and metadata tagging systems so datasets are well‑organized and self‑describing
• Integrate visualization tools (Grafana, Kibana, or similar) to enable engineers and scientists to explore and analyze experiment data
• Design data lifecycle policies including retention, archival, and compliance requirements
• Ensure data pipelines are reliable, idempotent, and observable with clear error handling and retry logic
• Work with engineering and science teams to define data schemas, access patterns, and query requirements

• Own deployment workflows for software delivered to embedded instruments in our central lab
• Manage release processes for a small number of complex, high‑value lab‑operated instruments
• Design deployment strategies that account for rollback, validation, and minimal downtime
• Coordinate versioned releases across multiple software components and dependencies
• Support development, staging, and production environment parity