Principal ML Solutions Architect - Token Factory

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 insurance coverage.

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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.

Base Compensation Range

$208,000—$261,000 USD