Principal ML Solutions Architect

The role sits within Nebius Token Factory, our serverless platform for running and customizing open-source LLMs in production. Token Factory enables serverless inference and fine-tuning (LoRA, full FT, RFT) with internal optimizations such as speculative decoding, quantization, cache-aware routing and dedicated endpoints. You will be 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 shape the platform roadmap with backend, product, and research teams. You may 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. 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, LLM fine-tuning including SFT/LoRA, LLM evaluation and deployment of LLM-powered applications. Strong Python programming skills and excellent communication. Nice-to-have: Contributions to OSS inference/ML projects, published research, multimodal AI, DevOps tooling, internal tooling for ML workflows. Preferred tech stack includes Python, vLLM, TensorRT-LLM, SGLang, Transformers, OpenAI/Anthropic SDKs, Kubernetes, Docker, cloud platforms. Key Employee Benefits and Pay Transparency outlined with salary: 208k-261k USD base.