ML Solutions Architect (Early Talent)

Details

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.

Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.

Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R&D hubs across Europe, the UK, North America and Israel.

Summary

• Location: Remote from USA
• Duration: 3 months
• Compensation: Paid
• Eligibility: Current University student (Computer Science or related field), Recent Graduate or Early Career specialist
• Work authorization: permitted to work in the job’s location

The Role

We're looking for an ML Solutions Architect (Early Career) to join the team behind Nebius Token Factory's serverless inference and fine-tuning platform for open-source LLMs. Working alongside senior Solutions Architects, you'll take on real technical work — building and testing LLM-based solutions, benchmarking, and inference optimization — and learn how scalable AI applications are built and tuned on our platform, in close collaboration with our backend team.

This is a hands-on learning role with close mentorship from senior SAs. Strong performers will be considered for a full-time Solutions Architect position at the end of the program.

This is a paid temporary contract, open to students and recent graduates. You're welcome to work remotely from any timezone.

Responsibilities

• Help build and test LLM-based solutions and applications using Token Factory's inference services, including multimodal models (text, vision, audio).
• Assist senior SAs with prompt engineering, model selection, benchmarking, and inference optimization.
• Run performance and quality experiments to support proof-of-concept work.
• Contribute to internal tooling and automation that improves how the SA team delivers.

Requirements

• Currently pursuing or recently completed a BSc/MSc/PhD in Computer Science, Machine Learning, or a related field.
• Strong Python programming skills.
• Hands-on generative AI experience, including with common ML frameworks (e.g., PyTorch, Transformers).
• Strong communication skills, with a willingness to explain technical concepts to diverse audiences.

Nice-to-haves

• Experience deploying/serving LLMs with vLLM, SGLang, or TensorRT-LLM.
• Familiarity with inference optimization techniques such as quantization, batching, caching, and routing.
• Knowledge of model architectures and fine-tuning approaches.
• Contributions to open-source ML/AI projects.

Preferred Technical Stack

• Programming Languages: Python.
• ML Frameworks and Libraries: vLLM, SGLang, TensorRT-LLM, Transformers, OpenAI/Anthropic SDKs.
• Frameworks for Agentic Pipelines: Langchain / Langsmith / smolagents / equivalent.
• API and Web Frameworks: FastAPI, Flask.
• MLOps and DevOps tools: Kubernetes (K8s), Docker, Git.
• Cloud Platforms: AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (Azure ML).

Key employee benefits in the US

• Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families.
• 401(k) plan: Up to 4% company match with immediate vesting.
• Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
• Remote work reimbursement: Up to $85/month for mobile and internet.
• Disability & life insurance: Company-paid short-term, long-term and life insurance coverage.

Benefits & Perks

• Competitive compensation
• Career growth and learning opportunities
• Flexibility and ownership
• Collaborative and innovative culture
• Opportunity to work on impactful AI projects
• International environment and talented teams

Pay Transparency

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.