The role 

Nebius Token Factory needs scientists who can turn frontier inference bottlenecks into research problems, publish credible work, and then help ship the results into production. This is not a papers-only research role. The Applied Scientist is expected to design rigorous experiments, write strong code, collaborate with engineers, and convert research into deployed inference capabilities.

A Senior Applied Scientist owns well-scoped research and production optimization projects. They can publish or prepare high-quality technical work while also producing code, experiments, and prototypes that engineers can use.

Your 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.

• 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 work with MLEs and 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 that build external credibility for Nebius Token Factory.

• Collaborate with MLE, GPU kernel, backend infrastructure, product, and customer teams to choose high-leverage research bets.

• Mentor engineers and scientists on experimental design, scientific rigor, and model/system tradeoffs.

Must-haves: 

• 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.

• Strong experimental design skills, including ablations, baselines, metrics, statistical reasoning, and failure analysis.

• Excellent written and verbal communication.

Nice-to-haves: 

• 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.

Key employee benefits in the US:

• Health insurance: 100% company-paid medical, dental, and vision coverage for employees and families.

• 401(k) plan: Up to 4% company match with immediate vesting.

• Parental leave: 20 weeks paid for primary caregivers, 12 weeks for secondary caregivers.

• Remote work reimbursement: Up to $85/month for mobile and internet.

• Disability & life insurance: Company-paid short-term, long-term and life insurance coverage.