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About the Role

Nebius Token Factory is building production-grade inference for open-source, customer, and internally optimized models. This role owns the path from model artifact to fast, reliable, cost-efficient endpoint, working at the intersection of model internals, inference engines, serving architecture, benchmarking, and production deployment.

Responsibilities

Own a platform-level optimization pillar such as quantization, speculative decoding, KV-cache optimization, engine selection, or long-context serving.
Create reusable evaluation, benchmarking, and rollout frameworks across multiple model families.
Provide technical direction to MLEs, Applied Scientists, GPU kernel engineers, and infrastructure engineers on shared performance goals.
Optimize LLM and VLM endpoints for latency, throughput, memory efficiency, GPU utilization, quality, and cost per token.
Deploy, configure, benchmark, and extend inference engines such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, or similar systems.
Build and productionize model-compression workflows, including quantization, quantization-aware training, distillation, low-bit serving, and accuracy recovery.
Implement or integrate speculative decoding, draft-model approaches, KV-cache optimization, prefix caching, chunked prefill, continuous batching, and disaggregated prefill/decode serving.
Build reproducible benchmark harnesses for TTFT, TPOT, tokens per second per GPU, p95/p99 latency, GPU memory, reliability, and cost per token.
Partner with GPU kernel engineers and platform engineers to diagnose bottlenecks across model code, kernels, runtime, scheduler, gateway, and cluster layers.
Write clear design docs, performance reports, rollout plans, and customer-facing technical explanations.

Requirements

Strong Python and PyTorch engineering skills.
Hands-on experience deploying or optimizing LLM, VLM, or high-throughput transformer inference systems.
Practical knowledge of at least one modern inference stack such as vLLM, SGLang, TensorRT-LLM, Triton Inference Server, NVIDIA Dynamo, Ray Serve, KServe, or equivalent internal systems.
Strong understanding of transformer inference bottlenecks, including KV cache, attention, memory bandwidth, batching, parallelism, and long-context serving.
Ability to reason quantitatively about latency, throughput, quality, utilization, and cost tradeoffs.
Strong communication skills and ability to collaborate with research, kernel, infrastructure, product, and customer teams.

Skills

Quantization
Speculative decoding
KV-cache optimization
Long-context serving
Evaluation frameworks
Benchmarking frameworks
Rollout frameworks
LLM optimization
VLM optimization
Latency optimization
Throughput optimization
Memory efficiency
GPU utilization
Cost per token optimization
vLLM
SGLang
TensorRT-LLM
Triton Inference Server
NVIDIA Dynamo
Model compression
Quantization-aware training
Distillation
Low-bit serving
Accuracy recovery
Draft-model approaches
Prefix caching
Chunked prefill
Continuous batching
Disaggregated prefill/decode serving
TTFT benchmarking
TPOT benchmarking
Tokens per second per GPU benchmarking
P95/p99 latency benchmarking
GPU memory benchmarking
Reliability benchmarking
Cost per token benchmarking
Design documentation
Performance reporting
Rollout planning
Customer-facing technical explanations
Quantization-aware training
Post-training quantization
FP8
INT8
INT4
NVFP4
MXFP4
AWQ
GPTQ
SmoothQuant
Distillation
Speculative decoding
EAGLE
Medusa
Multi-token prediction
Inference acceleration
Agentic workloads
Tool calling
Structured outputs
Streaming APIs
High concurrency
Multi-step orchestration
CUDA
Triton
vLLM contributions
SGLang contributions
TensorRT-LLM contributions
FlashInfer contributions
LMCache contributions
PyTorch contributions
Triton contributions
Ray contributions
KServe contributions

Benefits

Competitive compensation
Career growth and learning opportunities
Flexibility and ownership
Collaborative and innovative culture
Opportunity to work on impactful AI projects
International environment and talented teams

About the Company

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.

Equal Opportunity

Nebius is an equal opportunity employer.
We are committed to fostering an inclusive and diverse workplace and to providing equal employment opportunities in all aspects of employment.
We do not discriminate on the basis of race, color, religion, sex (including pregnancy), national origin, ancestry, age, disability, genetic information, marital status, veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by applicable law.
Applicants must be authorized to work in the country in which they apply and will be required to provide proof of employment eligibility as a condition of hire.
If you need accommodations during the application process, please let us know.