Senior Applied AI Solutions Engineer

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.

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.

Qualifications

• 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.
• Experience sharing technical work publicly — notebooks, talks, blog posts that people actually use.

Benefits

We offer competitive compensation and benefits packages. Actual compensation will be determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law.

Base Compensation Range: $200—$350,000 USD

Benefits & Perks:

• Competitive compensation
• Career growth and learning opportunities
• Flexible working arrangements
• Dynamic and collaborative work environment that values initiative and innovation
• Opportunity to work on impactful AI projects
• International environment and talented teams

What's It Like To Work At Nebius

Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI

Equal Opportunity Statement

Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law. Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.