ML Solution Architect (Early Talent)

About this role

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. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R&D.

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.

Your responsibilities:

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Help build and test LLM-based solutions and applications using Token Factory's inference services, including multimodal models (text, vision, audio).

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Assist senior SAs with prompt engineering, model selection, benchmarking, and inference optimization.

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Run performance and quality experiments to support proof-of-concept work.

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Contribute to internal tooling and automation that improves how the SA team delivers.

Must-haves:

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Currently pursuing or recently completed a BSc/MSc/PhD in Computer Science, Machine Learning, or a related field.

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Strong Python programming skills.

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Hands-on generative AI experience, including with common ML frameworks (e.g., PyTorch, Transformers).

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Strong communication skills, with a willingness to explain technical concepts to diverse audiences.

Nice-to-haves:

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Experience deploying/serving LLMs with vLLM, SGLang, or TensorRT-LLM.

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Familiarity with inference optimization techniques such as quantization, batching, caching, and…