Senior Applied AI Solutions Engineer

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. The role AI is moving faster than any single product team can track. Nebius is expanding across serverless, databases, MLflow, MLOps, Physical AI, and HCLS — and customers arriving with complex, real-world ML workloads need more than documentation. This role exists to close that gap: someone who can prototype what's possible, accelerate customers through their first 90 days, and feed hard-won field insight back into the product roadmap. This role sits at the intersection of deep ML engineering and product impact. You'll spend roughly half your time in the field — helping new customers move from POC to production, running technical onb [... source excerpt omitted ...] ou get your hands dirty every day. What success looks like in 12 months The product and sales teams have a library of working, polished demos they reach for on calls Enterprise customers you've touched have meaningfully faster time-to-value than those you haven't At least 2–3 product changes were shipped because of feedback you originated The team understands where applied AI is heading 6–12 months from now, partly because you told them Your responsibilities will include: Build prototypes and demos across the product portfolio — serverless inference, databases, MLflow, MLOps, and vertical use cases in Physical AI and HCLS — that become assets for sales, product, and engine [... source excerpt omitted ...] urn findings into working prototypes, writeups, and product recommendations Feed the product roadmap with specific, grounded feedback; be the voice of "here's what broke in three customer POCs last month and here's what needs to change" Develop reusable technical assets — notebooks, reference architectures, benchmark results — that reduce onboarding friction at scale We expect you to have: You've fine-tuned large models, debugged distributed training jobs, built production RAG or agentic pipelines, and optimized inference on GPU infrastructure — not just read about it You're fluent in the modern ML stack: PyTorch, HuggingFace, CUDA fundamentals, Kubernetes for ML, MLflow or