ML Solution Architect

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 <strong>paid temporary contract</strong>, open to students and recent graduates. You're welcome to work remotely from any timezone.

"Your 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.

"Must-haves:":

• 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).