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Senior/Staff System Research Engineer – LLM Inference Optimization

Snowflake

Senior/Staff System Research Engineer – LLM Inference Optimization is a still-open role at Snowflake on Jobrow. Location: Hybrid · US-WA-Bellevue · Bellevue · Washington · United States. Listed pay: $236k–$330k. Sourced from the company's public Ashby board. First seen 2 months ago; last confirmed 9 days ago. Apply on the employer ATS — Jobrow is not the employer.

Pay
$236k–$330k
Workplace
Hybrid
Location
US-WA-Bellevue · Bellevue · Washington · United States
First seen
2 months ago
Last seen
9 days ago
Board
Ashby
Apply

About this role

At Snowflake, we are powering the era of the agentic enterprise.

  • Function: Machine learning
  • Seniority: staff
  • tensorflow

Location & pay

Hybrid · US-WA-Bellevue · Bellevue · Washington · United States Listed pay on Jobrow: $236k–$330k.

Source

Jobrow reads the public Ashby board for Snowflake. First seen 2 months ago; last seen 9 days ago. We are not the employer — apply only on the company ATS.

Posting

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. You don’t just use tools; you possess an innate curiosity, treating AI as a high-trust collaborator that is core to how you solve problems and accelerate your impact. We look for low-ego individuals who thrive in dynamic and fast-moving environments and move with an experimental mindset — who rapidly test emerging capabilities to discover simpler, more powerful ways to deliver results. At Snowflake, your role isn't just to execute a function, but to help redefine the future of how work gets done. We are 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. Our mission is to build the next generation of high-performance and intelligent inference systems. We optimize not only how fast and efficiently models run, but also how quickly inference systems can adapt to new models, architectures, hardware, and workloads. Our work spans the full inference stack—from distributed serving and runtime systems to GPU kernels and model-system co-design. We explore techniques such as adaptive parallelism, speculative and parallel decoding, disaggregated inference, scheduling and batching, KV-cache optimization, model swapping, quantization, and GPU kernel optimization to push the frontier of latency, throughput, scalability, and cost. Beyond optimizing individual models, we are building intelligent and adaptive inference systems that can automate performance optimization—rapidly profiling new models and workloads, identifying bottlenecks, selecting effective execution strategies, and adapting system configurations with minimal manual tuning. We embrace AI-native engineering, using AI not only as the workload we optimize, but also as a tool to accelerate system development, experimentation, debugging, optimization, and adaptation to new models. Our goal is to accelerate both the speed of inference and the agility of inference development. This is an exciting opportunity to collaborate with a world-class team, including founding members of DeepSpeed, vLLM, and TensorFlow. Together, we will push the boundaries of AI systems and bring cutting-edge research into production-scale AI. Responsibilities - Design and develop high-performance LLM infe

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