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.
Responsibilities
•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
Requirements
•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
Nice to have
•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
Conditions
•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
•Competitive compensation and benefits packages; actual compensation determined based on job-related factors including experience, skills, qualifications, level, and geographic location, consistent with applicable law
•Career growth and learning opportunities
•Collaborative and innovative culture
•Opportunity to work on impactful AI projects
•International environment and talented teams
