The job, in the employer's words
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 routing.
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Knowledge of model architectures and fine-tuning approaches.
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Contributions to open-source ML/AI projects.
Preferred technical stack:
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Programming Languages – Python.
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ML Frameworks and Libraries – vLLM, SGLang, TensorRT-LLM, Transformers, OpenAI/Anthropic SDKs.
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Frameworks for Agentic Pipelines – Langchain / Langsmith / smolagents / equivalent.
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API and Web Frameworks – FastAPI, Flask.
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MLOps and DevOps tools – Kubernetes (K8s), Docker, Git.
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Cloud Platforms – AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (Azure ML).
Key employee benefits in the US:
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Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families.
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401(k) plan: Up to 4% company match with immediate vesting.
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Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
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Remote work reimbursement: Up to $85/month for mobile and internet.
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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 determined based on job-related factors, including experience, skills, qualifications, the level at which the candidate is hired, and geographic location, consistent with applicable law.
Benefits & Perks:
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams
What's it like to work at Nebius:
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI
Equal Opportunity Statement:
Nebius is an equal opportunity employer. We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment. We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.
Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.
If you need accommodations during the application process, please let us know.
Benefits mentioned
- 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.
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture
- Opportunity to work on impactful AI projects
- International environment and talented teams
Requirements mentioned
- 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.
- 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.
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