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Senior/Staff System Research Engineer – LLM Inference Optimization

Snowflake Inc.· Engineering
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Hybrid (US-WA-Bellevue), Hybrid (US-CA-Menlo Park)$236k - $330kFull-timePosted 1mo ago

About the role

Senior/Staff System Research Engineer focused on LLM inference optimization within Snowflake's AI Research team. develops high-performance inference systems, explores adaptive and AI-native approaches, and collaborates to deploy research innovations to production.

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.
  • Explore model-system co-design, including model or post-training techniques that unlock substantially more efficient inference.
  • Profile and benchmark end-to-end workloads to identify bottlenecks across compute, memory, communication, networking, scheduling, and model execution.
  • Collaborate closely with model researchers, infrastructure teams, and product teams to deploy research innovations in production.
  • Open-source and publish innovations through technical blogs and top-tier systems and machine learning conferences.

Qualifications

  • Bachelor’s degree in Computer Science, Electrical Engineering, or a related field. A Master’s degree or PhD is preferred.
  • 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.
  • Experience using AI-native engineering approaches to accelerate software development, experimentation, debugging, optimization, or system adaptation is a strong plus.
  • Excellent communication skills and the ability to collaborate effectively across research, engineering, and product teams.

Skills

Required:LLM inference optimizationDistributed serving and runtime systemsGPU programming (CUDA/Triton)Performance profiling tools
Nice to have:Model-system co-design
Last updated Sep 24, 2026. Interested? Apply on the company site.

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