AI Performance Optimization Engineer

Bright Vision Technologies is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. 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.

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