AI Performance Engineer

• 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.

Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position., * 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.

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.