Senior/Staff System Research Engineer – LLM Inference Optimization

Job summary

Responsibilities

At Snowflake, we are powering the era of the agentic enterprise. To usher in this new era, we seek AI-native thinkers across every function who are energized by the opportunity to reinvent how they work.

• 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…
• Develop adaptive and intelligent inference systems that automatically optimize execution for new model architectures, hardware platforms, workload characteristics, and deployment…
• Apply AI-driven and AI-native approaches to systems engineering, including automated profiling, bottleneck identification, configuration search, code generation, experimentation…

What they're looking for

• 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…

Tech stack

• vLLM
• TensorFlow
• CUDA
• Triton
• Snowflake