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.

Requirements

• 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.