Principal ML Solutions Architect - Token Factory

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.
The role
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. Beyond designing and implementing optimized inference and fine-tuning workflows, you will set technical direction across our largest and most strategic accounts, own the hardest performance and quality problems end to end, mentor other Solutions Architects, and serve as a primary technical voice shaping the platform roadmap with backend, product, and research teams.
You’re welcome to work remotely from the United States.
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
We expect you to have:
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
It would be an added bonus if you have:
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
Preferred technical stack:
Programming Languages: Python
ML Frameworks and Libraries: vLLM, TensorRT-LLM, SGLang, Transformers, OpenAI/Anthropic SDKs
MLOps and DevOps tools: Kubernetes (K8s), Docker, Git
Cloud Platforms: AWS (SageMaker, Bedrock), GCP (Vertex AI), Azure (Azure ML)
Key Employee 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 insur