Senior Applied AI Solutions Engineer
$0k - $350k • Remote • Amsterdam, Netherlands; Remote - Europe; Remote - United States • Full-time
Posted 7mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
Amsterdam, Netherlands; Remote - Europe; Remote - United States
Tech Stack
Remote Work Policy
Fully remote
Employment Type
Full-time
Categories
AI Engineer
About the job
Nebius is building a full-stack AI cloud platform to support developers and enterprises from data and model training through to production deployment. This role is crucial for bridging the gap between complex customer ML workloads and the platform's capabilities. You will prototype AI use cases, accelerate customer adoption, and provide valuable field insights to shape the product roadmap. This position blends deep ML engineering with significant product impact, involving hands-on work with customer ML stacks and building prototypes of emerging AI techniques.
Responsibilities
- Build prototypes and demos across the product portfolio (serverless inference, databases, MLflow, MLOps, Physical AI, HCLS) for sales, product, and engineering teams.
- Support new customers hands-on through POC design, technical onboarding, and validation, acting as a liaison between their ML team and the platform.
- Deeply explore emerging applied AI techniques (training, inference, agentic architectures, frameworks) and translate findings into prototypes, writeups, and product recommendations.
- Provide specific, grounded feedback to the product roadmap based on customer interactions and observed issues.
- Develop reusable technical assets like notebooks, reference architectures, and benchmark results to streamline customer onboarding.
Requirements
- Experience fine-tuning large models, debugging distributed training jobs, building production RAG or agentic pipelines, and optimizing inference on GPU infrastructure.
- Fluency in the modern ML stack including PyTorch, HuggingFace, CUDA fundamentals, Kubernetes for ML, MLflow or equivalent, and vector databases.
- Experience working with enterprise ML teams as a solutions engineer, customer engineer, or ML engineer collaborating closely with customers.
- Ability to read research papers and implement them to stay current with the field.
- Strong communication skills to explain technical concepts to both ML engineers and CTOs, considering cost implications.
Benefits
- Competitive salary and comprehensive benefits package.
- Opportunities for professional growth.
- Flexible working arrangements.
- Dynamic and collaborative work environment.
- Opportunity to work on impactful AI projects.
- International environment and talented teams.