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Forward Deployed Engineer - Physical AI Nebius · Remote - Europe
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Nebius

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Forward Deployed Engineer - Physical AI

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🗓️ Posted 2026-09-17 Remote - Europe Hybrid ICT
💰 Salary Not shared 📊 Market 6,500 GBP–10,000 GBP/mo
Location: Remote (SF Bay Area, CA or Austin, TX preferred) Work type: Remote Employment: Full-time

About the Company

Nebius is leading a new era in cloud infrastructure for the global AI economy, building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment. Built by engineers for engineers, the company owns hard problems across compute, storage, networking, and applied AI, from large-scale GPU orchestration to inference optimization. Listed on Nasdaq (NBIS) and headquartered in Amsterdam, Nebius has a global footprint with R&D hubs across Europe, the UK, North America, and Israel, with a team of 1,500+ including hundreds of engineers with deep expertise across hardware, software, and AI R&D.

About the Job

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. The role involves working alongside the Field CTO and Head of Physical AI, owning end-to-end technical execution inside each account: discovery, scoping, infrastructure design, build, and production rollout. Across accounts, the engineer turns repeated infrastructure pain into reusable platform capabilities and partners with Product and Engineering to fold them into the core platform.

Role & Responsibilities

  • ● 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.
  • ● Build and operate the cloud infrastructure that powers customer physical AI workflows, owning compute orchestration for simulation, training, evaluation, inference, and batch workloads at scale.
  • ● Build platform services for job execution, scheduling, retries, observability, logging, secrets, access control, and cost tracking, integrating Nebius cloud services into the product experience so infrastructure complexity is abstracted away from customers.
  • ● Build onboarding infrastructure for pilots, including sandbox environments, dataset storage, workflow execution, and deployment, ensuring early customer workloads run securely, isolated, observable, and reliable.
  • ● Optimize cloud cost, utilization, performance, and reliability across workloads, and debug infrastructure issues across application, network, storage, compute, and orchestration layers.
  • ● 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.
  • ● Help define the long-term infrastructure architecture for multi-tenant SaaS, enterprise deployments, and high-throughput physical AI workloads.
  • ● Turn repeated customer infrastructure pain into reusable platform capabilities, partnering with the Field CTO, Product, and Engineering teams to fold these into the core platform.
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  • ● Use modern AI coding tools (Claude Code, Codex, Cursor) as primary leverage to compress build timelines from weeks to days, treating engineering velocity as a primary success metric.
  • ● 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.

Requirements

Experience

  • ● 6+ years of hands-on engineering with strong backend, cloud infrastructure, platform engineering, or SRE experience, plus at least two years in a customer-facing or deployment-oriented technical role such as Forward Deployed Engineer, founding engineer, technical co-founder, or tech lead embedded with strategic customers.
  • ● Experience with Kubernetes, containers, CI/CD, observability, cloud networking, storage, IAM/RBAC, and infrastructure as code.

Skills

  • ● Experience building distributed systems, job orchestration, compute platforms, internal developer platforms, or ML infrastructure.
  • ● Strong Python, Go, or similar systems and backend programming skills.
  • ● Fluency in modern AI coding tools (Claude Code, Codex, Cursor) as primary leverage to rapidly design, implement, test, debug, and refactor production-quality software.
  • ● Familiarity with GPU workloads, batch jobs, training pipelines, inference workloads, or HPC-style compute environments.
  • ● Proven ability to debug infrastructure issues across application, network, storage, compute, and orchestration layers.
  • ● Strong instincts for isolation, RBAC, uptime, and traceability on workloads that touch customers.
  • ● Ability to navigate ambiguity without waiting for permission, with a bias toward simple, composable infrastructure that serves real customer workflows.
  • ● Strong written and verbal communication skills to hold technical conversations with customer CTOs and debrief design partner engagements to leadership.

Preferred (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.