Infrastructure Engineer, Security

About the Role

We're looking for an infrastructure engineer to own and evolve the security infrastructure that underpins our foundation models. In this role, you'll work across compute, storage, networking, and data platforms, making sure our systems are secure, reliable, and built to scale. You'll shape controls, architecture, and tooling so that security is part of how the platform works by default. You'll partner closely with research and product teams, enabling them to move quickly while keeping our models, data, and environments protected.

What You'll Do

• Architect security patterns for platforms and services, including network segmentation, service-to-service authentication, RBAC, and policy enforcement in Kubernetes and cloud environments.
• Manage identity, access, and secrets for humans and services: workload and cross-cloud identity, least-privilege IAM, and secrets management.
• Build secure platforms for data ingestion, processing, and curation: classification, encryption, access controls, and safe sharing patterns across teams.
• Write threat models and review designs with researchers and engineers to help them ship features and experiments in a safe, scalable way.
• Automate security checks and build guardrails: policy-as-code, secure infrastructure baselines, validation in CI/CD, and tools that make the secure path the easiest one.

Minimum Qualifications

• Bachelor's degree or equivalent experience in engineering, or similar.
• Strong background with containers and orchestration (e.g., Kubernetes) and how to secure them (namespaces, network policies, pod security, admission controls, etc.)
• Practical experience with Infrastructure as Code (Terraform or similar), including secure patterns for provisioning networks, IAM, and shared services.
• Solid understanding of cloud networking and security: VPCs, load balancers, service discovery, mTLS, firewalls, and zero-trust-style architectures.
• Proficiency with a systems language such as Rust and scripting in Python for building platform components and internal tools.
• Evidence of owning complex, production-critical systems, including debugging issues that span infra, security, and application layers.

Preferred Qualifications

• Experience with ML infrastructure, GPU clusters, or large-scale training environments (schedulers, job queues, shared storage, multi-tenant clusters).
• Background in AI labs, HPC environments, or ML-heavy organizations where both security and performance are first-class concerns.
• Experience profiling and tuning high-throughput systems, and an ability to reason about the cost of additional security layers.
• Talks, blogs, or publications on infrastructure security, distributed systems, or performance engineering.
• Open-source contributions to security, orchestration, observability, or infrastructure tooling.
• Familiarity with securing specialized hardware (GPUs, TPUs) and their integrations into training and inference pipelines.

Logistics

This role is based in San Francisco, California. We sponsor visas — if you're the right fit, we're committed to working through the visa process together.

Compensation & Benefits

• Depending on background, skills and experience, the expected annual salary range for this position is $200,000 - $475,000 USD.
• Generous health, dental, and vision benefits
• Unlimited PTO
• Paid parental leave
• Relocation support as needed