Senior Machine Learning Engineer, LLM Inference Optimization

The role 

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.

Your 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.

Must-haves: 

• 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 or