Role Description We are seeking an AI Performance Optimization 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. The ideal candidate has demonstrated impact on production AI workloads, with strong instrumentation and measurement discipline that enables rigorous, data-driven optimization decisions. In this role you will work closely with cross-functional partners — product, design, engineering, operations, and business stakeholders — to translate ambiguous requirements into well-engineered solutions, and will be expected to raise the bar through code review, design review, and mentorship of more junior engineers. The successful candidate brings strong engineering discipline, a clear communication style, and a track record of shipping meaningful work that holds up well in production. 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. 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. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] . Learn more about Bright Vision Technologies at www.bvteck.com .