Forward Deployed Engineer, Physical AI Infrastructure
Remote - Europe
Posted 5d ago
Job Location
Remote - Europe
Tech Stack
Remote Work Policy
On-site
Categories
AI Infrastructure Engineer
About the job
Nebius is building a full-stack AI cloud platform for the global AI economy, supporting developers and enterprises from data and model training through to production deployment. As a Forward Deployed Engineer, you will work alongside Physical AI customers to understand their workloads and production requirements, translating these into actionable insights for our core infrastructure products. You will also investigate and prototype new product opportunities based on unmet customer needs, helping to shape the future of Nebius' offerings. This hands-on role involves writing code, deploying and benchmarking models, building agents, and troubleshooting infrastructure in close collaboration with customers, NVIDIA, Product, and Engineering teams.
Responsibilities
- Collaborate with customer engineers to understand their workloads, infrastructure constraints, and production requirements.
- Translate Physical AI use cases into requirements for Cloud, Serverless, and Token Factory teams, supported by data and customer feedback.
- Identify enterprise adoption barriers and common needs across industries, working with Product and Engineering to test improvements.
- Develop agents for setting up, running, and debugging Physical AI workloads, integrating with clusters, schedulers, storage, and model servers.
- Deploy and benchmark open-weight models using serving stacks like vLLM or SGLang, optimizing them for customer workloads.
- Build prototypes and conduct technical evaluations to assess performance, reliability, and product fit.
- Assess market demand and existing alternatives for new product opportunities, and build prototypes where a clear market exists.
- Debug issues across Python, agents, model servers, Kubernetes, GPUs, networking, and storage, creating reusable scripts and guides from successful setups.
Requirements
- Three or more years of experience in software engineering, cloud infrastructure, DevOps, or ML infrastructure, including ownership of a production service, cluster, or platform.
- Experience working directly with customers or external engineers to define problems, conduct evaluations, and explain technical trade-offs.
- Ability to translate unfamiliar customer problems into concrete infrastructure requirements.
- Practical experience building with LLMs or agents, understanding their internals (e.g., transformers, KV caching, tool calling) and evaluation methods.
- Familiarity with enterprise infrastructure requirements such as identity and access management, network isolation, security, monitoring, and reliability.
- Strong Python and Linux skills, including code reading, automation, and debugging from logs and metrics.
- Practical experience with containers and Kubernetes, including configuration, networking, storage, and scheduling.
- Proven ability to deliver functional software with incomplete requirements, measure results, and follow through with users.
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