Senior Applied AI Solutions Engineer

Why work at Nebius Nebius is leading a new era in cloud computing to serve the global AI economy. We create the tools and resources our customers need to solve real-world challenges and transform industries, without massive infrastructure costs or the need to build large in-house AI/ML teams. Our employees work at the cutting edge of AI cloud infrastructure alongside some of the most experienced and innovative leaders and engineers in the field. Where we work Headquartered in Amsterdam and listed on Nasdaq, Nebius has a global footprint with RD hubs across Europe, North America, and Israel. The team of over 800 employees includes more than 400 highly skilled engineers with deep expertise across hardware and software engineering, as well as an in-house AI RD team. 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 onboarding, and working hands-on through their ML stack. The other half you'll spend building — prototyping applied AI use cases that show what's possible on the platform, going deep on emerging techniques before they're mainstream, and turning that expertise into concrete product direction. This is not a presales role. You 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 engineering teams Support new customers hands-on through POC design, technical onboarding, and validation; act as the bridge between their ML team and the platform during the critical first months Go deep on emerging applied AI — new training techniques, inference optimizations, agentic architectures, new frameworks — and turn 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 equivalent, vector databases You've worked with enterprise ML teams — whether as a solutions engineer, customer engineer, or an ML engineer who collaborated closely with customers You read papers and implement them — not for credit, but because it's how you stay sharp You communicate with calibration: you can explain activation checkpointing tradeoffs to an ML engineer in the morning and the cost implication to a CTO in the afternoon It will be an added bonus if you have: Experience in any of our vertical domains: Physical AI / robotics / simulation, ... (truncated, view full listing at source)