AI Performance Engineer

Job description

Job responsibilities

• Profile and optimize distributed training end to end - data loading and preprocessing, augmentation, kernel execution, gradient communication, and checkpointing
• Optimize large-scale offline and batch inference over petabyte-scale sensor logs: batching and scheduling strategies, quantization and low-precision execution, graph optimization, and accelerator saturation across…
• Establish roofline and performance models for our workloads, quantify the gap between achieved and theoretical performance, and stack-rank optimization opportunities by impact and effort
• Improve multi-node scaling efficiency: sharding and parallelism strategies, collective communication, interconnect utilization, and memory-bandwidth and kernel-fusion bottlenecks
• Drive cluster goodput - reduce GPU idle time from input pipeline stalls, storage and network I/O, scheduling gaps, stragglers, and failure recovery on long-running jobs
• Build the benchmarking, observability, and regression-detection tooling that keeps performance from silently degrading as models and code evolve
• Collaborate with engineers across functions to solve complex data and compute problems at scale
• Contribute to a team culture that values effective collaboration, technical excellence, and innovation

Job requirements

• Bachelor's degree or higher in computer science or adjacent fields.
• 1 or more years of hands-on experience with ML performance engineering: profiling, roofline analysis, throughput optimization, and root-cause investigation in production systems.
• 5 or more years of overall experience
• Experience with distributed multi-node training at scale (FSDP, DeepSpeed, Megatron, NCCL, or equivalent), including diagnosing scaling inefficiency as node count grows
• Deep familiarity with GPU or accelerator performance concepts - memory bandwidth, kernel launch overhead, occupancy, quantization, collective communication
• Experience with high-throughput or batch inference systems (NVIDIA Triton Inference Server, TensorRT, ONNX Runtime, Ray, or similar)
• Fluency in Python and proficiency in C++ or another systems language
• Excellent debugging, analytical, and problem-solving skills