Requirements

PhD in computer science, machine learning, ML systems, computer systems, computer architecture, electrical engineering, applied mathematics, or a closely related discipline.
Strong publication record or equivalent research artifacts in ML, ML systems, efficient inference, model compression, quantization, distillation, serving systems, or related areas.
Strong Python and PyTorch implementation skills, capable of turning ideas into experiments and working prototypes.
Deep knowledge of LLMs, VLMs, transformer inference, decoding algorithms, model compression, quantization, and production-serving trade-offs.
Strong experimental design skills including ablations, baselines, metrics, statistical reasoning, and failure analysis.
Excellent written and verbal communication skills.

Nice to haves

First-author publications at venues such as NeurIPS, ICML, ICLR, MLSys, ACL, EMNLP, ASPLOS, OSDI, SOSP, ISCA, or HPCA.
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 applying post-training, SFT, DPO, RLHF, RLAIF, preference optimization, or synthetic data generation to inference quality or efficiency.
Open-source research artifacts, widely used benchmarks, technical blogs, or invited talks demonstrating contributions to efficient AI systems.
Research or implementation experience in distributed inference system architecture such as LLM request routing, PDD, multi-node serving, or MoE expert parallelism including WideEP.

Leading research projects in efficient LLM and VLM inference, covering hypotheses, experiments, ablations, prototypes, and production handoff.
Developing and evaluating methods including quantization, quantization-aware training, distillation, speculative decoding, KV-cache reuse and compression, long-context inference, MoE routing, and model/runtime co-optimization.
Building prototypes with PyTorch, Triton, CUDA-adjacent tools, or inference-serving frameworks, collaborating with machine learning engineers and platform teams to productionize components.
Researching LLM request routing and scheduling strategies like cache-aware load balancing and prefill–decode disaggregation; evaluating effects on latency, throughput, and serving cost.
Investigating multi-node inference for dense and mixture-of-experts models, focusing on expert placement, load imbalance, and computation/communication trade-offs to guide architecture choices.
Developing rigorous evaluations on quality, latency, throughput, numerical stability, memory footprint, tail latency, and cost per token, comparing serving architectures under consistent conditions.
Collaborating with MLE, GPU kernel, backend infrastructure, product, and customer teams to prioritize research with measurable production impact.
Sharing results via internal reports, technical blogs, papers, and open-source artifacts, and mentoring engineers and scientists on experimental design and model/system trade-offs.