Nebius

Senior Applied Scientist, Efficient LLM Inference & Model Optimization

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SeniorZdalnie·LondynFull-time

#448258·Dodano miesiąc temu·2

Źródło: Nebius

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Tech Stack / Keywords

LLMVLMPyTorchCUDATritonPythonQuantizationQATMoENVIDIA

Firma i stanowisko

Nebius is building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, focusing on large-scale GPU orchestration, inference optimization, compute, storage, networking, and applied AI. The company is listed on Nasdaq (NBIS), headquartered in Amsterdam, and has R&D hubs across Europe, the UK, North America, and Israel with a team of over 1,500 engineers.

Wymagania

PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied math, or a closely related field.
Strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas.
Strong hands-on coding ability in Python and PyTorch, with ability to move from idea to experiment to prototype quickly.
Deep understanding of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving tradeoffs.
Strong experimental design skills including ablations, baselines, metrics, statistical reasoning, and failure analysis.
Excellent written and verbal communication.

Nice to have:

First-author publications in NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, HPCA, or comparable venues.
Experience deploying ML models or inference optimizations in production.
Experience with vLLM, SGLang, TensorRT-LLM, NVIDIA Dynamo, FlashAttention, FlashInfer, Triton, CUDA, or PyTorch internals.
Experience with post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation when connected to inference quality or efficiency.
Open-source research artifacts, widely used benchmarks, high-quality technical blogs, or invited talks in efficient AI systems.

Obowiązki

Own focused research projects from hypothesis through experiment, ablation, prototype, and production handoff.
Prepare internal reports, technical blogs, or papers when the work achieves external credibility.
Partner directly with MLEs to ensure research prototypes become usable production components.
Define and execute research programs in efficient LLM and VLM inference with measurable production impact.
Invent, evaluate, and productionize methods for quantization, QAT, distillation, speculative decoding, KV-cache reuse, KV-cache compression, long-context inference, MoE routing, and model/runtime co-optimization.
Build high-quality prototypes in PyTorch, Triton, CUDA-adjacent tooling, or inference-serving frameworks, then collaborate with platform engineers to productionize them.
Design rigorous evaluation methodology covering quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token.
Publish papers, technical reports, blog posts, and open-source artifacts.
Collaborate with MLE, GPU kernel, backend infrastructure, product, and customer teams to select high-leverage research projects.
Mentor engineers and scientists on experimental design, scientific rigor, and model/system tradeoffs.

Inne informacje

Applicants must be authorized to work in the country in which they apply and provide proof of employment eligibility as a condition of hire. Nebius is an equal opportunity employer committed to providing equal employment opportunities without discrimination.

Nebius

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