ML Solution Architect (Early Talent)
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:
-
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).
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.
Pay Transparency
We offer competitive compensation and benefits packages. Actual compensation will be det