ML Infra Engineer, Platform
Remote • San Francisco • FullTime
Posted 9d ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
AI Infrastructure Engineer
About the job
Physical Intelligence is building general-purpose AI for the physical world, developing foundation models and learning algorithms for robots and future physically-actuated devices. The Infrastructure team is central to this mission, building and operating the backbone for training state-of-the-art VLA models, large-scale simulation, and reliable deployment of intelligence across robot fleets. This role involves collaborating closely with researchers, robotics runtime, product, and platform engineers to ensure infrastructure scales from prototype to production-grade deployments.
Responsibilities
- Own and scale AI-native infrastructure, including operating and evolving Kubernetes clusters and service deployment patterns.
- Build a scalable microservice platform for internal systems like evaluation services, operational tooling, and internal APIs.
- Drive security, observability, and cost-aware infrastructure by treating them as first-class primitives and building systems for early detection of reliability and performance issues.
- Improve cost visibility and enable cost-aware decision-making at the infrastructure level.
- Harden platform foundations with multi-cloud considerations, designing authentication/authorization flows, networking architecture, quota/rate-limiting services, and predictable cloud primitives.
- Reduce infrastructure churn by standardizing patterns, abstractions, and interfaces.
- Improve developer experience by building clear, documented interfaces for platform infrastructure usage and supporting consistent local vs. remote development workflows.
- Collaborate with researchers and engineers to understand requirements and translate them into reusable infrastructure.
- Own systems end-to-end, from design through operation.
Requirements
- Strong first-principles thinking.
- Familiarity with agent infrastructure, observability, security, and sandboxing.
- Deep experience with cloud platforms (GCP, AWS) and distributed systems, including compute orchestration, networking, autoscaling, service meshes, and 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.
- 4-6 years of experience working in fast-moving or early-stage environments with demonstrated growth trajectory.