ML Solution Architect (Early Talent)

nebius · United States · United States

<div class="content-intro">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.</div><h3>Summary:</h3> 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 <h3> The role </h3> 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:  <ul> <li> Help build and test LLM-based solutions and applications using Token Factory's inference services, including multimodal models (text, vision, audio).  </li> <li> Assist senior SAs with prompt engineering, model selection, benchmarking, and inference optimization.  </li> <li> Run performance and quality experiments to support proof-of-concept work.  </li> <li> Contribute to internal tooling and automation that improves how the SA team delivers.  </li> </ul> Must-haves:  <ul> <li> Currently pursuing or recently completed a BSc/MSc/PhD in Computer Science, Machine Learning, or a related field.  </li> <li> Strong Python programming skills.  </li> <li> Hands-on generative AI experience, including with common ML frameworks (e.g., PyTorch, Transformers).  </li> <li> Strong communication skills, with a willingness to explain technical concepts to diverse audiences.  </li> </ul> Nice-to-haves:  <ul> <li> Experience deploying/serving LLMs with vLLM, SGLang, or TensorRT-LLM.  </li> <li> Familiarity with inference optimization techniques such as quantization, batching, caching, and routing.  </li> <li> Knowledge of model architectures and fine-tuning approaches.  </li> <li> Contributions to open-source ML/AI projects.  </li> </ul> Preferred technical stack: </spa

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