AI Performance Engineer

Key Responsibilities

• Profile and optimize end-to-end AI training and inference pipelines for throughput, latency, and cost
• Identify and eliminate bottlenecks across data loading, model compute, communication, and memory
• Implement and tune quantization, sparsity, and pruning strategies to reduce model footprint and accelerate inference
• Tune distributed training using tensor parallelism, pipeline parallelism, FSDP, and ZeRO-style sharding
• Implement KV cache optimization, continuous batching, and speculative decoding for LLM serving
• Drive compiler-level optimizations using Triton, XLA, TorchInductor, or TVM, working with the broader ML framework community to land improvements that translate into measurable end-to-end performance gains
• Optimize data pipelines, sharding strategies, and storage access patterns for high-throughput training
• Build and maintain rigorous benchmark suites and regression frameworks across workloads
• Collaborate with ML and platform engineering teams to embed best practices in standard pipelines
• Drive cost-efficiency improvements through model architecture, hardware selection, and scheduling strategies
• Evaluate new hardware and software offerings, and advise on adoption
• Document performance tuning playbooks and share findings broadly across engineering teams
• Stay current with AI systems research and translate advances into production improvements

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related field
• Six or more years of experience in performance engineering, ML systems, or HPC
• Strong proficiency in Python and C++
• Hands-on experience optimizing deep learning workloads on modern GPUs
• Deep understanding of distributed training and inference techniques
• Experience with profiling tools across CPU, GPU, and distributed systems
• Familiarity with model compression techniques and their accuracy implications
• Strong grasp of memory hierarchies, communication primitives, and parallelism strategies
• Excellent measurement, debugging, and analytical reasoning skills
• Strong communication and collaboration skills

Preferred Qualifications

• Experience optimizing LLM inference at production scale
• Contributions to vLLM, TensorRT-LLM, DeepSpeed, or similar projects
• Familiarity with custom kernel authoring in Triton or CUTLASS
• Experience with FinOps for AI workloads
• Publications or talks on AI systems performance