Senior Applied AI Solutions Engineer

<div class="content-intro"><p><strong>About Nebius:</strong></p> <p>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.</p> <p>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.</p> <p>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.</p></div><div><strong>The role</strong></div> <div>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.<br>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.<br>This is not a presales role. You get your hands dirty every day.</div> <div><br><strong>What success looks like in 12 months</strong></div> <div> <ul> <li>The product and sales teams have a library of working, polished demos they reach for on calls</li> <li>Enterprise customers you've touched have meaningfully faster time-to-value than those you haven't</li> <li>At least 2–3 product changes were shipped because of feedback you originated</li> <li>The team understands where applied AI is heading 6–12 months from now, partly because you told them</li> </ul> </div> <div><strong>Your responsibilities will include:</strong></div> <ul> <li>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</li> <li>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</li> <li>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</li> <li>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"</li> <li>Develop reusable technical assets — notebooks, reference architectures, benchmark results — that reduce onboarding friction at scale</li> </ul> <div><strong>We expect you to have:</strong></div> <ul> <li>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</li> <li>You're fluent in the modern ML stack: PyTorch, HuggingFace, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent, vector databases</li> <li>You've worked with enterprise ML teams — whether as a solutions engineer, customer engineer, or an ML engineer who collaborated closely with customers</li> <li>You read papers and implement them — not for credit, but because it's how you stay sharp</li> <li>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</li> </ul> <div><strong>It will be an added bonus if you have:</strong></div> <ul> <li>Experience in any of our vertical domains: Physical AI / robotics / simulation, HCLS (drug discovery, medical imaging, clinical NLP), or enterprise AI application development</li> <li>Familiarity with MLOps at scale (Kubeflow, Metaflow, Argo, Ray)</li> <li>Prior work at a cloud provider or AI infrastructure company</li> <li>You've shared technical work publicly — notebooks, talks, blog posts that people actually use</li> </ul> <div><strong>Who thrives here</strong></div> <div>You'll thrive here if you're energized by variety — one day deep in a customer's MLOps stack, the next building a demo from scratch. You want your technical depth to influence product decisions, not just close deals.</div> <div> </div> <div><strong>What we offer</strong></div> <ul> <li>Competitive salary and comprehensive benefits package.</li> <li>Opportunities for professional growth within Nebius.</li> <li>Flexible working arrangements.</li> <li>A dynamic and collaborative work environment that values initiative and innovation.</li> </ul> <div>We're growing and expan