Forward Deployed Engineer, Physical AI Infrastructure
About this role
Embedding directly inside strategic customer engineering teams, you will design, build, and ship production infrastructure that allows users to run real physical AI workloads instead of simple demos. Working alongside the Field CTO and the Head of Physical AI, the position requires managing end-to-end technical execution from initial discovery and scoping to infrastructure design and production rollout. Daily responsibilities involve translating ambiguous infrastructure problems into robust systems, optimizing compute orchestration for simulation and training, and turning repeated customer pain points into reusable capabilities for the core cloud platform.
Nebius operates a full-stack AI cloud platform designed to support developers and enterprises through model training and production deployment without standard infrastructure complexity. The engineering organization owns complex challenges across compute, storage, networking, and applied AI to make cloud resources fast, reliable, secure, and cost-effective. Ideal candidates demonstrate high agency, fluency in AI-native development workflows, and a strong background in backend, cloud infrastructure, or site reliability engineering roles.
What you will do
- Own end-to-end technical execution within strategic accounts, handling discovery, scoping, infrastructure design, build, and production rollout.
- Build and operate cloud infrastructure and compute orchestration for simulation, training, evaluation, inference, and batch workloads at scale.
- Develop platform services for job execution, scheduling, retries, observability, logging, secrets, access control, and cost tracking.
- Construct onboarding infrastructure for pilots, including sandbox environments, dataset storage, and workflow execution.
- Optimize cloud cost, utilization, performance, and reliability across workloads while debugging issues across application, network, storage, compute, and orchestration layers.
- Partner closely with the Physical AI Systems and Platform & Product FDEs to expose infrastructure capabilities through clean APIs, SDKs, and workflows.
- Utilize modern AI coding tools like Claude Code, Codex, and Cursor as primary leverage to compress build timelines from weeks to days.
What you need
- Bring 6+ years of hands-on engineering experience in backend, cloud infrastructure, platform engineering, or SRE, with at least two years in a customer-facing or deployment-oriented technical role.
- Demonstrate experience building distributed systems, job orchestration, compute platforms, internal developer platforms, or machine learning infrastructure.
- Possess strong systems and backend programming skills using Python, Go, or similar languages.
- Show fluency in modern AI coding tools such as Claude Code, Codex, and Cursor for rapid software development and debugging.
- Have experience with cloud-native toolchains including Kubernetes, containers, CI/CD, observability, cloud networking, storage, IAM/RBAC, and infrastructure as code.
- Maintain familiarity with GPU workloads, batch jobs, training pipelines, inference workloads, or high-performance computing environments.
- Exhibit proven ability to debug infrastructure issues across application, network, storage, compute, and orchestration layers.
Nice to 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.
- Familiarity with NVIDIA GPU infrastructure, CUDA workloads, Isaac Sim, Omniverse, or simulation-at-scale.
Benefits
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams
Skills and tech stack
Questions candidates ask
Where is this role located?
This position offers remote work from Europe. Candidates are also welcome to work remotely from the United States with a preference for the SF Bay Area, CA or Austin, TX.
What tools and frameworks will I use daily?
You will work with modern AI coding tools like Claude Code, Codex, and Cursor as primary leverage. The tech stack also involves Kubernetes, Python, Go, and various orchestration and GPU computing tools.
Who will I work with on the team?
You will sit with strategic customers and ISV partners while working alongside the Field CTO and the Head of Physical AI. Additionally, you will partner closely with the Physical AI Systems and Platform & Product FDEs.
What are the core expectations for engineering velocity?
You are expected to treat engineering velocity as a primary success metric by leveraging modern AI coding tools. This approach helps compress build timelines from weeks down to days.