Senior Applied Scientist, Efficient LLM Inference & Model Optimization
Nebius
About the position
Join a leading tech startup focused on AI cloud infrastructure, working on impactful AI projects and collaborating with engineers.
Tech stack
- python
- pytorch
- ml
- cuda
- quantization
- distillation
Requirements
Required:
- 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; 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
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
Responsibilities
- Own focused research projects from hypothesis through experiment, ablation, prototype, and production handoff
- Prepare internal reports, technical blogs, or papers when the work is externally credible
- 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
Benefits
- Competitive compensation
- Career growth and learning opportunities
- Flexibility and ownership
- Collaborative and innovative culture