Junior ML Solution Architect (Early Talent)

Title: ML Solution Architect (Early Talent) Location: United States

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.