AI Performance Engineer

Job Summary

We are seeking an AI Performance Engineer to focus on extracting maximum throughput, minimizing latency, and reducing cost across training and inference workloads for large neural network systems. The role spans the full stack from low-level kernel optimization to distributed system tuning, requiring deep understanding of GPU architecture, model parallelism, memory management, and compiler-level optimization.

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
• Optimize distributed training using tensor parallelism, pipeline parallelism, FSDP, and ZeRO-style sharding
• Tune attention implementations using FlashAttention, paged attention, and related techniques
• 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