Staff Engineer, CI/CD & Cloud Infrastructure
hace 12 días
San Diego
Job Description Location: San Diego, CA Job Type: Full-Time Salary Range: $175,000 - $185,000 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. This is a senior hands-on role for an engineer who thrives at the intersection of DevOps automation, cloud infrastructure management, and release engineering. You will design and maintain CI/CD pipelines, manage complex AWS infrastructure as code, and ensure full traceability from source commits through builds, tests, artifacts, and deployments. You will work cross-functionally with firmware, application, and HPC engineers to keep the entire delivery pipeline fast, reliable, and observable. Key Responsibilities CI/CD & Build Engineering • Design, build, and maintain CI/CD pipelines using GitHub Actions or similar platforms, • Manage build systems for Python, C/C++, and CUDA codebases on Linux, • Integrate build tools (CMake, Make, pip, setuptools) 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 Cloud Infrastructure (AWS) • 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 failover strategies, • Data workflow and pipeline engines (Step Functions, Airflow, or similar), • Compute services (EC2, ECS, EKS, Lambda) scaled to workload requirements, • 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 On-Premises HPC & Hybrid Infrastructure • 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 filesystems, 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 Experiment Data Management & Pipelines • 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/ OpenSearch) 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 Deployment & Release Engineering • 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 Logging, Observability & Traceability • Implement centralized log collection and aggregation across cloud and on-site systems, • Deploy and manage observability tooling (Prometheus, Grafana, Loki, CloudWatch, or similar), • Ensure structured, searchable logging with clear correlation across services, • Build dashboards and alerting for infrastructure health, pipeline status, and deployment state, • Establish traceability standards linking builds, tests, artifacts, and deployments, • Support diagnostics and post-mortem analysis for production incidents AI-Augmented DevOps • Integrate agentic AI tools into CI/CD workflows to automate code review, test generation, and pipeline troubleshooting, • Evaluate and deploy AI-powered assistants for infrastructure management, incident response, and operational tasks, • Design guardrails and human-in-the-loop controls for AI-driven automation in production environments, • Stay current with the rapidly evolving landscape of AI-augmented development and DevOps tooling, • Champion adoption of agentic AI across engineering workflows to accelerate delivery and improve reliability Qualifications Education: • BS/MS in Computer Science or Engineering Required: Experience & Technical Skills • 7+ years of experience in DevOps, CI/CD, or cloud infrastructure roles, • Strong, hands-on Linux expertise (administration, debugging, performance tuning), • Deep experience designing and operating CI/CD pipelines (GitHub Actions preferred), • Proven experience managing complex AWS infrastructure at scale, • Strong knowledge of Docker including multi-stage builds, registries, and orchestration, • Experience with infrastructure as code using Terraform, • Experience with Kubernetes and Helm for container orchestration, • Solid understanding of versioning strategies, artifact management, and release engineering, • Experience integrating agentic AI into DevOps workflows and CI/CD pipelines Programming & Build Systems • Proficiency in Python and shell scripting for automation and tooling, • Ability to read, debug, and build C/C++ and CUDA applications on Linux, • Experience integrating build systems (CMake, Make) into CI pipelines, • Familiarity with package management and dependency resolution across languages Cloud & Infrastructure • Deep AWS experience across IAM, networking (VPC, security groups), storage, compute, and database services, • Experience managing on-premises Linux HPC infrastructure alongside cloud resources, • Experience designing for high availability, failover, and disaster recovery, • Experience with data pipeline and workflow orchestration tools (Step Functions, Airflow, or similar), • Experience with search and indexing platforms (Elasticsearch, OpenSearch, or similar), • Understanding of tiered storage strategies and data lifecycle management, • Knowledge of cost management, tagging strategies, and infrastructure governance Observability & Traceability • Experience with logging and monitoring stacks (Prometheus,, • Grafana, Loki, ELK, or CloudWatch), • Understanding of build and artifact traceability practices, • Experience with structured logging and distributed tracing concepts Preferred: • Experience deploying software to embedded or lab-operated instruments, • Experience with high-speed networking (InfiniBand, RDMA, or 10/25/100GbE) in HPC environments, • Experience with CUDA build toolchains and GPU-accelerated workloads, • Familiarity with Azure or GCP in addition to AWS, • Experience in regulated or reliability-sensitive environments, • Experience with GitOps workflows and progressive delivery strategies, • Familiarity with secrets management (Vault, AWS Secrets Manager) We are an equal opportunity employer. We thrive on diversity and collaboration.