Senior Machine Learning Engineer, LLM Inference Optimization

Nebius·Zurichsenior

In short

→Ingeniero ML senior enfocado en optimizar inferencia de LLM/VLM para reducir costos y mejorar rendimiento.
→Trabajas con motores de inferencia (vLLM, TensorRT-LLM, Triton), optimizas latencia, throughput y uso de GPU en producción.
→Destacado: trabajas en soluciones de vanguardia como speculative decoding, chunked prefill y servidores desagregados.

Proficiency in English required for collaboration with global teams.

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The questions they'll ask you

1. ¿Cómo optimizarías el uso de GPU en un endpoint de LLM con alta carga de prefill y baja latencia?

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💵 USD · Remote · No visa

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What they ask for

✓5+ años de experiencia en ingeniería de ML o inferencia de modelos
✓Experiencia comprobada con motores de inferencia como vLLM, SGLang o TensorRT-LLM
✓Conocimiento profundo de técnicas de compresión de modelos: cuantización, distillación, entrenamiento consciente de cuantización
✓Habilidades sólidas en Python y C++ para optimización de alto rendimiento
✓Experiencia en benchmarking reproducible y despliegue en producción
✓Capacidad para resolver problemas complejos de rendimiento en entornos de producción real

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

vLLMSGLangTensorRT-LLMTriton Inference ServerNVIDIA DynamoPythonC++CUDAGPU orchestrationLLM inference optimization

Who should you write to at Nebius?

Your free dossier identifies the people who'd interview you — their background, what they value, and how to reach out so you stand out before applying.

About Nebius: 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. The role Nebius Token Factory is building fast, reliable, and cost-efficient inference services for frontier models. As a Senior Machine Learning Engineer on our Applied AI team, you will own model and endpoint optimization from model artifacts through production deployment. Your work will span model internals, inference engines, serving architecture, and benchmarking, with a focus on improving latency, throughput, memory efficiency, GPU utilization, and cost per token while maintaining model quality and reliability. This is a hands-on role in which you will work on complex optimization projects, diagnose difficult serving problems, and deliver measurable improvements in production. Working closely with kernel and platform engineers, you will evaluate serving configurations, resolve performance and quality regressions, and optimize inference for real-world workloads, supported by reproducible benchmarks and safe production rollouts. 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 har

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