Senior AI Systems Engineer: LLM Inference & Optimization

Showcify; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Snowflake is seeking talented systems developers and researchers to advance LLM inference systems and optimization in the AI Research team. You will build high-performance, adaptive inference stacks spanning distributed serving, kernels, and runtime optimization to accelerate model deployment and experimentation.

You will collaborate with researchers and engineers to push latency, throughput, and cost boundaries while enabling rapid support for new models and hardware.

Qualifications

• Bachelor’s degree in Computer Science, Electrical Engineering, or a related field.
• 5+ years of experience in 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.

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.

Job description

Snowflake is seeking talented systems developers and researchers to advance LLM inference systems and optimization in the AI Research team. You will build high-performance, adaptive inference stacks spanning distributed serving, kernels, and runtime optimization to accelerate model deployment and experimentation.

You will collaborate with researchers and engineers to push latency, throughput, and cost boundaries while enabling rapid support for new models and hardware.