Senior Machine Learning Engineer, LLM Inference Optimization

Nebius•Palo Alto, CA

8d•$195,200 - $262,200•Remote

About The Position

Nebius Token Factory is building an AI training and model post-training capability for frontier model improvement. This role owns the infrastructure that makes large-scale training and RL experiments possible, reliable, reproducible, and efficient. The work sits at the intersection of distributed systems, GPU performance, model training frameworks, RL pipelines, and production engineering. A Senior MLE owns substantial model and endpoint optimization projects end to end. They are deeply hands-on, can debug difficult serving problems independently, and can deliver measurable improvements without needing heavy supervision.

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.

Nice To Haves

Experience with quantization-aware training, post-training quantization, FP8, INT8, INT4, NVFP4, MXFP4, AWQ, GPTQ, SmoothQuant, or related techniques.
Experience with distillation, speculative decoding, EAGLE, Medusa, multi-token prediction, or other inference acceleration methods.
Experience with agentic workloads, including tool calling, structured outputs, streaming APIs, high concurrency, and multi-step orchestration.
CUDA or Triton familiarity, even if the role is not primarily a kernel-engineering role.
Open-source contributions to vLLM, SGLang, TensorRT-LLM, FlashInfer, LMCache, PyTorch, Triton, Ray, KServe, or related projects.

Responsibilities

Own optimization work for specific model families, customer endpoints, or serving backends.
Run engine comparisons and recommend practical serving configurations for specific workloads.
Debug model quality or performance regressions during production rollouts.
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.

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
100% company-paid medical, dental, and vision coverage for employees and families.
Up to 4% company match with immediate vesting.
20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.
Up to $85/month for mobile and internet.
Company-paid short-term, long-term and life insurance coverage.