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 (Forward Deployed Engineer, founding engineer, technical co-founder, tech lead embedded with strategic customers, or equivalent)
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: You navigate ambiguity without waiting for permission, with a bias toward simple, composable infrastructure that serves real customer workflows over scheduling another meeting
Communication: Strong written and verbal communication. You can hold your own in a technical conversation with a customer CTO and debrief a design partner engagement to the Head of Physical AI
Prior experience as a Forward Deployed Engineer or an equivalent customer-embedded engineering function at a frontier company
Experience with Nebius, AWS, GCP, Azure, Lambda Labs, or other AI cloud infrastructure
Experience with Slurm, Soperator, Kubernetes GPU scheduling, Ray, Argo, Airflow, Metaflow, or similar orchestration tools
Experience with ML training infrastructure, model serving, simulation workloads, or large-scale data pipelines
Experience supporting enterprise customers, design partners, or production pilots
Familiarity with NVIDIA GPU infrastructure, CUDA workloads, Isaac Sim, Omniverse, or simulation-at-scale