Senior/Staff System Research Engineer – LLM Inference Optimization
US-WA-Bellevue · US-CA-Menlo Park · 5+ years experience · Full-time · Hybrid · Posted Sep 21, 2026
At a glance
- Experience
- 5+ years experience
- Employment
- Full-time
- Workplace
- Hybrid
- Location
- US-WA-Bellevue · US-CA-Menlo Park
About the role
- Snowflake is looking for talented systems developers and researchers to join the Snowflake AI Research team and advance the state of the art in LLM inference systems and optimization.
- The role involves building the next generation of high-performance and intelligent inference systems that optimize model speed, efficiency, and adaptability.
- Work spans the full inference stack from distributed serving and runtime systems to GPU kernels and model-system co-design.
Responsibilities
- Design and develop high-performance LLM inference systems, spanning distributed serving, runtime systems, GPU execution, and performance-critical kernels.
- Develop novel techniques to improve inference latency, generation speed, throughput, memory efficiency, scalability, and cost.
- Explore advanced inference techniques including speculative and parallel decoding, prefill/decode disaggregation, adaptive parallelism, continuous batching and scheduling, KV-cache management, quantization, and communication optimization.
- Develop adaptive and intelligent inference systems that automatically optimize execution for new model architectures, hardware platforms, workload characteristics, and deployment environments.
- Apply AI-driven and AI-native approaches to systems engineering, including automated profiling, bottleneck identification, configuration search, code generation, experimentation, runtime strategy selection, debugging, and performance tuning.
- Independently identify high-impact performance and systems problems, formulate hypotheses, prototype solutions, and drive promising ideas from research through production.
- Design distributed inference strategies across GPUs and nodes, including tensor, sequence, pipeline, data, and expert parallelism.
- Develop efficient approaches for multi-model serving, dynamic resource management, model loading and swapping, and workload-aware scheduling.
- Analyze and optimize GPU kernels and operators for attention, MoE, communication, and other performance-critical model components.
- Collaborate closely with model researchers, infrastructure teams, and product teams to deploy research innovations in production.
Requirements
- Bachelor’s degree in Computer Science, Electrical Engineering, or a related field.
- 5+ years of experience in one or more of the following areas: LLM inference systems, distributed AI systems, GPU systems, or high-performance computing.
- Strong understanding of modern LLM inference architectures and the performance tradeoffs involved in serving large-scale models.
- Hands-on experience with modern LLM inference and serving frameworks, such as vLLM, SGLang, TensorRT-LLM, or similar systems.
- Experience designing, extending, or optimizing inference runtimes, including areas such as scheduling, batching, KV-cache management, distributed execution, parallelism, speculative decoding, or disaggregated serving.
- Strong understanding of GPU architectures and experience with CUDA, Triton, or similar GPU programming environments.
- Experience with performance-oriented libraries and frameworks such as CUTLASS, cuBLAS, cuDNN, or related technologies.
- Experience profiling and diagnosing end-to-end system performance using Nsight Systems, Nsight Compute, or equivalent tools.
- Demonstrated ability to operate as an independent problem identifier and solver—recognizing important problems with limited direction, defining the right technical questions, and driving solutions through ambiguity.
- Strong ability to work across model, runtime, distributed system, and hardware layers and reason about end-to-end performance tradeoffs.
Preferred qualifications
- A Master’s degree or PhD
- Experience using AI-native engineering approaches to accelerate software development, experimentation, debugging, optimization, or system adaptation
Skills
- vLLM
- SGLang
- TensorRT-LLM
- CUDA
- Triton
- CUTLASS
- cuBLAS
- cuDNN
- Nsight Systems
- Nsight Compute
- DeepSpeed
- TensorFlow
