Senior ML Solutions Architect - Token Factory
Technologies
LLM · Langchain · Langsmith · smolagents · vLLM · SGLang · TensorRT-LLM · Transformers · Python · RAG
About Nebius
Full-stack AI cloud infrastructure platform delivering GPU compute and software to hyperscalers and enterprises without requiring in-house AI/ML teams.
Public · 1000–2000 people
Job description
The full responsibilities and requirements are on the employer’s site.
Read the job description ↗Source and classification
Implementation & delivery · Evidence for this classification:
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 seek an experienced Senior ML Solutions Architect to support customers leveraging Nebius Token Factory's serverless inference and fine-tuning platforms for open-source LLMs across multiple modalities. In this role, you will be collaborating with clients to design and implement optimized inference workflows, build customized LLM-based solutions and architect scalable AI applications using our served models. You will also work closely with our backend team to improve our platform to match clients' needs. You’re welcome to work
More from the job description
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 seek an experienced Senior ML Solutions Architect to support customers leveraging Nebius Token Factory's serverless inference and fine-tuning [... source excerpt omitted ...] laborating with clients to design and implement optimized inference workflows, build customized LLM-based solutions and architect scalable AI applications using our served models. You will also work closely with our backend team to improve our platform to match clients' needs. You’re welcome to work remotely from the United States. Your responsibilities will include: Optimize LLM inference across various modalities to drive business value and support customer goals Provide support in supervised and reinforcement learning fine-tuning to maximize model quality for the customers Design and implement LLM-based solutions using Nebius Token Factory’s inference services Build prod [... source excerpt omitted ...] io) and domain-specific models Provide technical expertise in prompt engineering, RAG architectures and model selection Collaborate with product and engineering teams to surface customer feedback and shape the platform roadmap Guide customers in scaling from POC to production with a focus on performance, reliability, and cost efficiency We expect you to have: 5+ years of experience in ML/AI systems, with at least 2 years focused on LLMs and generative AI Deep knowledge of the LLM ecosystem, including model architectures and fine-tuning approaches Hands-on experience with: Running LLMs in production: deploying and operating inference workloads LLM fine-tuning, including s
Employer postings · Data from · Sources