Forward Deployed Engineer – Physical AI Cloud Platform

Responsibilities

• End-to-End ownership inside strategic accounts: own discovery, technical scoping, infrastructure design, build, and production rollout for each design partner and ISV engagement.
• Cloud infrastructure & compute orchestration: build and operate cloud infrastructure that powers customer physical AI workflows.
• Platform services: build platform services for job execution, scheduling, retries, observability, logging, secrets, access control, and cost tracking.
• Customer onboarding infrastructure: build onboarding infrastructure for pilots including sandbox environments and secure execution.
• Reliability, security & cost: optimize cloud cost, utilization, performance, and reliability across workloads.
• Cross-FDE partnership: partner with Physical AI Systems FDE to support heavy simulation pipelines.
• Long-term architecture: define the long‑term infrastructure architecture for multi‑tenant SaaS and high‑throughput physical AI workloads.
• Pattern codification & productization: turn customer infrastructure pain into reusable platform capabilities.
• Rapid engineering velocity: use modern AI coding tools to compress build timelines.
• Field enablement & feedback loops: co-author reference architectures, solution templates, and technical blogs for broader field support.

• 6+ years of hands‑on engineering: strong backend, cloud infrastructure, platform engineering, or SRE experience, with at least two years in a customer‑facing or deployment‑oriented technical role.
• Distributed systems & compute platforms: experience building distributed systems, job orchestration, compute platforms, internal developer platforms, or ML infrastructure.
• Strong systems programming: strong Python, Go, or similar systems and backend programming skills.
• AI‑native development workflow: fluency in modern AI coding tools (Claude Code, Codex, Cursor) as primary leverage to rapidly design, implement, test, debug, and refactor production‑quality software.
• Cloud‑native toolchain: experience with Kubernetes, containers, CI/CD, observability, cloud networking, storage, IAM/RBAC, and infrastructure as code.
• GPU & HPC workloads: familiarity with GPU workloads, batch jobs, training pipelines, inference workloads, or HPC‑style compute environments.
• Cross‑layer debugging: proven ability to debug infrastructure issues across application, network, storage, compute, and orchestration layers.
• Security & reliability instincts: strong instincts for isolation, RBAC, uptime, and traceability on workloads that touch customers.
• High agency: navigate ambiguity without waiting for permission, with a bias toward simple, composable infrastructure that serves real customer workflows.
• Communication: strong written and verbal communication for technical discussions.

Demonstrates expertise in building and optimizing cloud infrastructure for AI workflows, with a strong focus on distributed systems, backend programming, and customer‑facing technical roles. Proficient in leveraging modern AI coding tools and cloud‑native technologies to enhance reliability, security, and performance across multi‑tenant SaaS environments.

• Kubernetes
• CI/CD
• Cloud Networking
• IAM/RBAC
• Observability Tools
• Storage Solutions