Forward Deployed Engineer, Physical AI Infrastructure

Nebius Group; Amsterdam; Remote; EUR 90,000 - 140,000; Full time

Benefits offered by this job

Competitive compensation

Career growth and learning

Flexibility and ownership

Collaborative and innovative culture

International environment

Job summary

Nebius is hiring a Forward Deployed Engineer, Physical AI Infrastructure to work with enterprise customers, shape core infrastructure, and test new ideas. You will translate workloads into concrete requirements for multiple teams and develop prototypes to test unmet needs.

You will deploy and benchmark models, collaborate with Product and Engineering, and help drive opportunities from concept to initial product. This role blends hands-on coding with customer-facing impact.

Qualifications

• Three+ years in software engineering, cloud infrastructure, DevOps, ML infra or similar with ownership of a production service or platform.
• Experience working directly with customers to scope problems, run evaluations, and explain tradeoffs.
• Ability to translate unfamiliar customer problems into concrete infrastructure requirements.
• Practical experience building with LLMs or agents and understanding of their internals and evaluation methods.
• Familiarity with enterprise infra needs including IAM, network isolation, security, monitoring, and reliability.
• Strong Python and Linux skills with ability to read unfamiliar code, automate setups, and debug from logs.

Responsibilities

• Work with customer engineers to understand workloads, constraints, and production requirements.
• Translate use cases into requirements for Cloud, Serverless, and Token Factory teams with evidence.
• Identify adoption barriers and test improvements with customers.
• Build agents to set up, run, and debug Physical AI workloads connected to clusters, storage, and model servers.
• Deploy and benchmark open-weight models with serving stacks and tune for workloads.
• Build prototypes and run evaluations to answer questions on performance, reliability, and product fit.
• Assess demand for opportunities outside current product teams and pursue where there is a clear market.
• Debug across Python, agents, model servers, Kubernetes, GPUs, networking, and storage, turning setups into reusable scripts.

Skills

Python

Linux

Kubernetes

LLMs/agents

Cloud infrastructure

Customer-facing

Tools

vLLM

SGLang

TensorRT-LLM

NVIDIA CUDA

Job description

About Nebius:

Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.

Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.

• Remote - Europe

The role

Nebius is hiring a Forward Deployed Engineer, Physical AI Infrastructure to work alongside Physical AI customers, shape our core infrastructure products, and test ideas for what we should build next.

You will work with enterprises, ISVs, and digital-native companies running training, simulation, synthetic data, evaluation, and model serving workloads. By understanding their systems and helping them reach production, you will uncover requirements that inform our Cloud, Serverless, and Token Factory teams. The goal is to connect needs across Physical AI industries with product decisions, including improvements that benefit enterprise customers more broadly.You will also investigate opportunities for new Nebius products. You will develop hypotheses about unmet needs, examine demand and existing alternatives, and build prototypes to test with customers. Your findings will help determine which opportunities deserve investment and what an initial product should deliver.The work is hands-on. You will write code, deploy and benchmark models, build agents, and troubleshoot infrastructure alongside customer engineers.The position is part of the Physical AI go-to-market team, with close collaboration across customers, NVIDIA, Product, and Engineering.

What you will do

• Work with customer engineers to understand their workloads, infrastructure constraints, and production requirements.
• Translate Physical AI use cases into requirements that Cloud, Serverless, and Token Factory teams can use, backed by measurements and customer evidence.
• Identify enterprise adoption barriers and common needs across industries. Work with Product and Engineering to assess gaps and test improvements with customers.
• Build agents that set up, run, and debug Physical AI workloads, connecting them to clusters, schedulers, storage, and model servers.
• Deploy and benchmark open-weight models with serving stacks such as vLLM or SGLang, then tune them for customer workloads.
• Build prototypes and run technical evaluations to answer specific questions about performance, reliability, and product fit.
• Assess demand and existing alternatives for opportunities outside current product teams’ scope, then build where there is a clear market.
• Debug across Python, agents, model servers, Kubernetes, GPUs, networking, and storage. Turn successful setups into reusable scripts, deployments, and short guides.

What we are looking for

• Three or more years in software engineering, cloud infrastructure, DevOps, ML infrastructure, or similar, including ownership of a production service, cluster, or platform.
• Experience working directly with customers or external engineers to scope problems, run evaluations, and explain technical tradeoffs.
• Ability to turn an unfamiliar customer problem into concrete infrastructure requirements.
• Practical experience building with LLMs or agents, at work or independently, with an understanding of their internals (e.g., transformers, KV caching, or tool calling) and how to evaluate their outputs.
• Familiarity with enterprise infrastructure requirements, including identity and access management, network isolation, security, monitoring, and reliability.
• Strong Python and Linux skills: you can read unfamiliar code, automate a setup, and debug from logs and metrics.
• Practical experience with containers and Kubernetes, including configuration, networking, storage, and scheduling.
• A record of getting useful software working with incomplete requirements, measuring results, and following through with the people using it.

Helpful experience

• Enterprise customer engineering, solutions architecture, consulting, or taking a new product from an initial customer problem to adoption.
• Distributed compute such as Ray, Slurm, Spark, or multi-GPU jobs.
• NVIDIA GPU workloads, including memory management, drivers, CUDA, NCCL, and monitoring.
• Model serving with vLLM, SGLang, TensorRT-LLM, or TGI.
• Robotics, simulation, synthetic data, reinforcement learning, world models, or tools such as Isaac Sim, Isaac Lab, Omniverse, and Cosmos.
• Infrastructure automation and enterprise integrations, including Terraform, CI/CD, private connectivity, and identity federation.

Benefits & Perks:

• Competitive compensation
• Career growth and learning opportunities
• Flexibility and ownership
• Collaborative and innovative culture
• Opportunity to work on impactful AI projectsInternational environment and talented teams