Forward Deployed Engineer – Physical AI

Job not on LinkedIn

🕒 September 17

🏄 California, Texas – Remote

🏄 California – Remote 🤠 Texas – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

🤖 Artificial Intelligence

👻 Ghost score 31%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Nebius Group

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

🤖 Artificial Intelligence

🏢 Enterprise

☁️ SaaS

Artificial Intelligence • Enterprise • SaaS

Nebius Group is building one of the world’s leading AI infrastructure companies, focusing on providing the necessary compute, storage, and tools for developers in the AI space. Based in Europe and listed on Nasdaq, Nebius has a global presence with R&D centers across Europe, North America, and Israel. The company's primary offering is an AI-centric cloud platform designed for intensive AI workloads, complemented by various other businesses involved in generative AI development, edtech, and autonomous technology.

📋 Description

• Own discovery, technical scoping, infrastructure design, build, and production rollout for strategic customer and ISV engagements • Build and operate cloud infrastructure for simulation, training, evaluation, inference, and batch workloads • Build platform services for job execution, scheduling, retries, observability, logging, secrets, access control, and cost tracking • Integrate Nebius cloud services into customer product experiences • Build secure, isolated, observable, and reliable onboarding infrastructure for pilots • Optimize cloud cost, utilization, performance, and reliability • Debug issues across application, network, storage, compute, and orchestration layers • Partner with Physical AI Systems and Platform & Product FDEs through APIs, SDKs, and product workflows • Help define infrastructure architecture for multi-tenant SaaS, enterprise deployments, and high-throughput physical AI workloads • Convert repeated customer infrastructure problems into reusable platform capabilities • Use AI coding tools to accelerate production engineering • Co-author reference architectures, solution templates, and technical blogs; maintain feedback loops with Field CTO, Product, and Engineering teams

🎯 Requirements

• 6+ years of hands-on engineering experience in backend, cloud infrastructure, platform engineering, or SRE • At least 2 years in a customer-facing or deployment-oriented technical role • 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 Claude Code, Codex, and Cursor for AI-native development • Experience with Kubernetes, containers, CI/CD, observability, cloud networking, storage, IAM/RBAC, and infrastructure as code • 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 • Strong written and verbal communication • Applicants must be authorized to work in the country in which they apply and provide proof of employment eligibility • Additional bonus experience includes Forward Deployed Engineering, Nebius/AWS/GCP/Azure/Lambda Labs, Slurm/Soperator/Kubernetes GPU scheduling/Ray/Argo/Airflow/Metaflow, ML training infrastructure, enterprise customers, NVIDIA GPU infrastructure, CUDA, Isaac Sim, or Omniverse

🏖️ 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 • Trust and real ownership • Opportunity to shape the future of AI