ML Infra Engineer, Platform
San FranciscoFull-timePosted 1w ago
About the role
ML Infra Engineer responsible for owning and scaling AI-native infrastructure, driving security and observability, hardening platform foundations, improving developer experience, and collaborating across teams to deploy and operate ML infrastructure for physical robotics applications.
Responsibilities
- Own and scale AI-native infrastructure: operate and evolve Kubernetes clusters and service deployment patterns, build scalable microservice platform for internal systems with agent-use as primary interface, support safe rollouts, upgrades, and rollback strategies.
- Drive security, observability and cost-aware infrastructure: implement security, logging, metrics, tracing, and alerting as platform primitives; surface reliability and performance issues; improve cost visibility and cost-aware decision-making at infrastructure level.
- Harden platform foundations: own core infrastructure with multi-cloud considerations; design authentication/authorization flows, networking architecture, quota and rate-limiting services, and cloud primitives; reduce infra churn by standardizing patterns and interfaces.
- Improve developer experience: build clear, documented interfaces for platform infrastructure; enable consistent local vs. remote development workflows and self-serve infrastructure usage.
- Collaborate and lead through ownership: work with researchers and engineers to translate fast-moving needs into reusable infrastructure; own systems end-to-end from design through operation.
Qualifications
- Extremely strong first-principles thinking.
- Familiarity with agent infrastructure, observability, security and sandboxing.
- Deep experience with cloud platforms (GCP, AWS) and distributed systems: compute orchestration, networking, autoscaling, service meshes, load balancing.
- Ability to reason about system bottlenecks, performance tuning, and cost optimizations across compute, networking, and storage.
- Comfort with Kubernetes, cluster-level reliability, and service-oriented architectures.
- Solid intuition around scalability, performance, and failure modes.
- Experience with infrastructure-as-code (e.g. Terraform), containerization, and modern platform engineering practices.
- Familiarity with logging, metrics, tracing, incident response, SLOs, and debugging complex distributed systems.
- Strong cross-functional communication and ownership mindset.
- Experience (4-6 years) working in fast-moving or early-stage environments where ambiguity is normal with demonstrated growth trajectory.
Skills
Required:KubernetesCloud platforms (GCP, AWS)Infrastructure as codeObservability and monitoring
Last updated Oct 14, 2026. Interested? Apply on the company site.
About Physical Intelligence
Physical Intelligence (π)
Physical Intelligence is bringing general-purpose AI into the physical world.
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