Principal ML Solutions Architect

Role Description

This position sits within Nebius Token Factory, our serverless platform for running and customizing open-source LLMs in production. Token Factory allows for serverless inference and fine-tuning (LoRA, full FT, RFT) backed by in-house optimizations like custom speculative decoding, quantization, cache-aware routing, and dedicated endpoints. Customers come to us to move from prototype to scaled production without the cost and complexity of building and tuning their own inference stack.

We're looking for a Principal ML Solutions Architect to act as the most senior technical authority for customers leveraging Token Factory's serverless inference and fine-tuning platforms. Your responsibilities will include:

• Own the most complex, highest-stakes customer engagements from architecture through production across multiple modalities, driving measurable business value.
• Optimize LLM inference at the framework and hardware level and codify the resulting best practices into reusable playbooks for the team.
• Lead supervised and reinforcement fine-tuning efforts to maximize model quality.
• Design and implement production-ready LLM solutions using Token Factory's inference services.
• Provide deep technical expertise in prompt engineering, RAG architectures, model selection, and cost/performance trade-offs at scale.
• Partner closely with product, engineering, and research to surface customer needs, prototype platform features, and directly influence the roadmap.
• Guide customers from PoC to production with a focus on performance, reliability, and cost efficiency — and define the standards by which the team does so.
• Mentor Senior and mid-level Solutions Architects; raise the technical bar of the team through review, enablement, and knowledge sharing.
• Represent Token Factory externally through talks, blog posts, and conferences.

Qualifications

• 8+ years of experience in ML/AI systems, with at least 4 years focused on LLMs and generative AI.
• Demonstrated technical leadership: owning ambiguous, high-impact problems end to end and influencing decisions across teams and customers.
• Expert knowledge of the LLM ecosystem: model architectures, fine-tuning approaches, and inference internals.
• Deep, hands-on command of inference optimization: quantization, KV-cache management, batching, routing, etc.
• Hands-on experience with running LLMs in production at scale: deploying, operating, and debugging inference workloads down to the framework level.
• LLM fine-tuning, including SFT/LoRA and data preparation/curation; experience with RL-based fine-tuning.
• LLM evaluation: building task-specific benchmarks and offline/online eval pipelines, including LLM-as-a-judge setups.
• Inference frameworks and libraries (vLLM, SGLang, TensorRT-LLM), including the ability to read, modify, and contribute to their internals.
• Deploying LLM-powered applications using APIs from OpenAI, Anthropic, or open-source models.
• Strong Python programming skills.
• Excellent communication skills, with the ability to clearly explain technical concepts to diverse audiences, from engineers to executives.

Requirements

• Contributions or maintainership in major OSS inference/ML projects (vLLM, SGLang, TensorRT-LLM).
• Published research, conference talks, or widely-read technical writing in the LLM/serving space.
• Deep work with multimodal AI models (vision-language, speech).
• Proficiency with DevOps tooling (Docker, Kubernetes) and infrastructure-as-code.
• Experience building or owning internal tooling/automation for ML workflows at scale.

Benefits

• 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 and benefits packages.
• Career growth and learning opportunities.
• Flexibility and ownership.
• Collaborative and innovative culture.
• Opportunity to work on impactful AI projects.
• International environment and talented teams.