AI Systems Research and Development Engineer – LLM Inference Systems & Optimization

Required

• 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.