Note: The job is a remote job and is open to candidates in USA. Nebius is building a full-stack AI cloud platform for developers and enterprises, supporting workloads from data and model training through production deployment. The Senior Applied AI Solutions Engineer will prototype applied AI use cases, support customers through POCs and onboarding, investigate emerging ML techniques, develop reusable technical assets, and provide feedback that informs the product roadmap.
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
Skills
- 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
- Applicants must be authorized to work in the country in which they apply
- 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
Benefits
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams
Company Overview