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

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

Experience in any of our vertical domains: Physical AI / robotics / simulation, HCLS (drug discovery, medical imaging, clinical NLP), or enterprise AI application development
Familiarity with MLOps at scale (Kubeflow, Metaflow, Argo, Ray)
Prior work at a cloud provider or AI infrastructure company
You've shared technical work publicly — notebooks, talks, blog posts that people actually use

Competitive salary and comprehensive benefits package.
Opportunities for professional growth within Nebius.
Flexible working arrangements.
A dynamic and collaborative work environment that values initiative and innovation.

Senior Applied AI Solutions Engineer