Forward Deployed Engineer - Physical AI

Description

The role

The Forward Deployed Engineer, Cloud Platform is a senior, high-autonomy individual contributor role that owns the infrastructure foundation making the physical AI platform fast, reliable, scalable, secure, and cost-effective. This role sits with strategic customers and ISV partners, embedded directly inside their engineering teams, and ships production infrastructure that lets customers run real physical AI workloads, not just demos. Your job is to make the platform feel like a product, not a collection of cloud scripts. 

You will work alongside the Field CTO and the Head of Physical AI, and partner closely with the Physical AI Systems and Platform & Product FDEs. Inside each account, you own end-to-end technical execution: discovery, scoping, infrastructure design, build, and production rollout. Across accounts, you turn repeated infrastructure pain into reusable platform capabilities and partner with Product and Engineering to fold them into the core platform. Your field work is the primary input to the Nebius Physical AI roadmap. 

You are welcome to work remotely from the United States (SF Bay Area, CA or Austin, TX preferred). 

Your responsibilities will include: 

• End-to-End Ownership Inside Strategic Accounts: Own discovery, technical scoping, infrastructure design, build, and production rollout for each design partner and ISV engagement, translating ambiguous infrastructure problems into deployable production systems. 

• Cloud Infrastructure & Compute Orchestration: Build and operate the cloud infrastructure that powers customer physical AI workflows. Own compute orchestration for simulation, training, evaluation, inference, and batch workloads, not just what runs, but how it runs at scale. 

• Platform Services: Build platform services for job execution, scheduling, retries, observability, logging, secrets, access control, and cost tracking. Integrate Nebius cloud services into the product experience so infrastructure complexity is abstracted away from customers. 

• Customer Onboarding Infrastructure: Build onboarding infrastructure for pilots, including sandbox environments, dataset storage, workflow execution, and deployment, and make sure early customer workloads run for real: secure, isolated, observable, and reliable. 

• Reliability, Security & Cost: Optimize cloud cost, utilization, performance, and reliability across workloads, and debug infrastructure issues across application, network, storage, compute, and orchestration layers, wherever the failure actually lives. 

• Cross-FDE Partnership: Partner with the Physical AI Systems FDE to support GPU-heavy simulation, training, and evaluation pipelines, and with the Platform & Product FDE to expose infrastructure capabilities through clean APIs, SDKs, and product workflows. 

• Long-Term Architecture: Help define the long-term infrastructure architecture for multi-tenant SaaS, enterprise deployments, and high-throughput physical AI workloads. 

• Pattern Codification & Productization: Turn repeated customer infrastructure pain into reusable platform capabilities. Partner with the Field CTO, Product, and Engineering teams to fold these into the core platform. Treat every engagement as a forcing function for the next ten. 

• Rapid Engineering Velocity: Use modern AI coding tools (Claude Code, Codex, Cursor) as primary leverage. Compress build timelines from weeks to days. Treat engineering velocity as a primary success metric. 

• Field Enablement & Feedback Loops: Co-author reference architectures, solution templates, and technical blogs for the broader Nebius field, and maintain structured channels to ensure customer learnings flow back to the Field CTO, Product, and Engineering teams. 

We expect you to have: 

• 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. 

It would be an added bonus if you have: 

• 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.