Responsibilities About the Role We are seeking a systems researcher or engineer with deep expertise in large-scale distributed storage and caching infrastructure to design and maintain a high-performance KV cache layer for large language model (LLM) inference. This role focuses on improving latency, throughput, and cost-efficiency in transformer-based model serving by optimizing the reuse of attention key-value states and prompt embeddings. You'll work on cutting-edge AI systems problems with real-world impact, alongside a world-class team. The role offers opportunities to publish, contribute to open-source, attend top conferences, and enjoy competitive compensation, generous research resources, and an innovation-driven culture. Responsibilities - Design and implement a distributed KV cache system to store and retrieve intermediate states (e.g., attention keys/values) for transformer-based LLMs across GPUs or nodes. - Optimize low-latency access and eviction policies for caching long-context LLM inputs, token streams, and reused embeddings. - Collaborate with inference and serving teams to integrate the cache with token streaming pipelines, batched decoding, and model parallelism. - Develop cache consistency and synchronization protocols for multi-tenant, multi-request environments. - Implement memory-aware sharding, eviction (e.g., windowed LRU, TTL), and replication strategies across GPUs or distributed memory backends. - Monitor system performance and iterate on caching algorithms to reduce compute costs and response time for inference workloads. - Evaluate and, where needed, extend open-source KV stores or build custom GPU-aware caching layers (e.g., CUDA, Triton, shared memory, RDMA). Qualification Minimum Qualifications - PhD in Computer Science, Applied Mathematics, Electrical Engineering, or a related technical field. - Strong understanding of transformer-based model internals and how KV caching affects autoregressive decoding. - Experience with distributed systems, memory management, and low-latency serving (RPC, gRPC, CUDA-aware networking). - Familiarity with high-performance compute environments (NVIDIA GPUs, TensorRT, Triton Inference Server). - Proficiency in languages like C++, Rust, Go, or CUDA for systems-level development. Preferred Qualifications - Prior experience building inference-serving systems for LLMs (e.g., vLLM, SGLang, FasterTransformer, DeepSpeed, Hugging Face Text Generation Inference). - Experience with memory hierarchy optimization (HBM, NUMA, NVLink) and GPU-to-GPU communication (NCCL, GDR, GDS, InfiniBand). - Exposure to cache-aware scheduling, batching, and prefetching strategies in model serving. Job Information 【For Pay Transparency】Compensation Description (Annually) The base salary range for this position in the selected city is $153900 - $300960 annually. Compensation may vary outside of this range depending on a number of factors, including a candidate's qualifications, skills, competencies and experience, and location. Base pay is one part of the Total Package that is provided to compensate and recognize employees for their work, and this role may be eligible for additional discretionary bonuses/incentives, and restricted stock units. Benefits may vary depending on the nature of employment and the country work location. Employees have day one access to medical, dental, and vision insurance, a 401(k) savings plan with company match, paid parental leave, short-term and long-term disability coverage, life insurance, wellbeing benefits, among others. Employees also receive 10 paid holidays per year, 10 paid sick days per year and 17 days of Paid Personal Time (prorated upon hire with increasing accruals by tenure). The Company reserves the right to modify or change these benefits programs at any time, with or without notice.