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Salary: USD 208,000 - 261,000 / annual
Nebius is a Nasdaq-listed AI cloud infrastructure company (headquartered in Amsterdam, with 1,500+ employees globally) building a full-stack platform for data, model training, and production deployment. Token Factory is their serverless platform for running and customizing open-source LLMs in production, offering serverless inference and fine-tuning (LoRA, full fine-tuning, reinforcement fine-tuning) backed by in-house optimizations like speculative decoding, quantization, cache-aware routing, and dedicated endpoints.
You will serve as the most senior technical authority for customers leveraging Token Factory's serverless inference and fine-tuning platforms. This is a high-impact individual contributor role with mentorship responsibilities. Your scope includes designing and implementing optimized inference and fine-tuning workflows for the company's largest and most strategic accounts, owning the hardest performance and quality problems end to end, mentoring other Solutions Architects, and serving as a primary technical voice shaping the platform roadmap with backend, product, and research teams.
Key responsibilities:
- 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 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
- 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
Requirements:
- 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, 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
Bonus qualifications:
- 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: Python; ML frameworks and libraries (vLLM, TensorRT-LLM, SGLang, Transformers, OpenAI/Anthropic SDKs); MLOps and DevOps tools (Kubernetes, Docker, Git); cloud platforms (AWS SageMaker/Bedrock, GCP Vertex AI, Azure ML).
