DevOps Engineer, Infrastructure & Platforms
Ricursive Intelligence1 month ago
Palo Alto, CA, USAMid Level
Responsibilities
- Design, build, and extend the self-managed cloud platform and infrastructure-as-code architecture with Terraform.
- Own day-to-day platform deployments across multiple environments, including performance, security, reliability, and scalability.
- Design, implement, and operate CI/CD pipelines in GitHub Actions for mission-critical repositories and reproducible artifact delivery.
- Architect scalable AI tooling and developer-experience workflows across distinct user environments in collaboration with physical design engineers.
- Build an observability stack covering pipeline health, application metrics, and ML workloads.
Requirements
- Bachelor’s degree in computer science, computer engineering, electrical engineering, or a closely related technical field, or equivalent practical experience.
- 4+ years of hands-on infrastructure or platform engineering experience with ownership of systems others depend on.
- Experience owning production infrastructure-as-code architecture, including maintenance and new features.
- Experience designing and implementing production CI/CD pipelines that build and ship reproducible artifacts with attention to performance, scalability, and security.
- Experience with observability tooling for pipeline health and application-level metrics.
- Preferred: hands-on experience with GCP, Kubernetes, and GitHub Actions, including custom runner setups.
- Preferred: experience running ML training and evaluation infrastructure, including GPU/TPU compute.
- Preferred: familiarity with LLM observability, tracing, evaluations, and cost and latency monitoring.
- Preferred: security experience involving dependency supply-chain hardening, OIDC-based authentication, least-privilege secrets, SOC 2, or penetration testing.
- Preferred: early-stage startup experience building infrastructure from zero or near-zero.
Tech Stack
Categories
About Ricursive Intelligence
Ricursive Intelligence builds AI systems that co-optimize machine learning models and the chips that run them, starting with AI-driven chip design tools for semiconductor and AI hardware teams. Its business focuses on software and research that closes the loop between model performance and silicon layout to accelerate development cycles. Privately held and founded in 2025, it operates at the intersection of AI infrastructure and semiconductor design.