> Markdown version of [/jobs/ext/2178198-ai-performance-engineer](https://www.wearedevelopers.com/jobs/ext/2178198-ai-performance-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Performance Engineer - **Company:** Bright Vision Technologies - **Location:** Shrewsbury, MA, United States (Remote available) - **Experience:** Expert - **Salary:** $100,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, C++ (Programming Language), Profiling, Computer Engineering, Extract Transform Load (ETL), Software Debugging, Distributed Computing Environment, Distributed Systems, Python (Programming Language), Performance Tuning, Graphics Processing Unit (GPU), Large Language Models, Deep Learning, Information Technology, Machine Learning Operations, TensorRT, Data Pipelines - **Published:** August 22, 2026 - **Apply:** https://www.careerjet.com/jobad/usecc1b845271539cc8e1dd071f91deaee ## About the Role 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. ## Description * 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. ## Related Videos - [Tour de Force: Open-Source LLM Inference Optimization from Simple to Sophisticated](https://www.wearedevelopers.com/videos/100099-tour-de-force-open-source-llm-inference-optimization-from-simple-to-sophisticated) - [Profiling Symfony & PHP apps with Blackfire](https://www.wearedevelopers.com/videos/265-profiling-symfony-php-apps-with-blackfire) - [Getting Started with Machine Learning](https://www.wearedevelopers.com/videos/260-getting-started-with-machine-learning) - [Shipping Faster with Less: Render on Cloud Hosting, AI Workloads, and the Future of DevOps](https://www.wearedevelopers.com/videos/1894-shipping-faster-with-less-render-on-cloud-hosting-ai-workloads-and-the-future-of-devops) - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [Trends, Challenges and Best Practices for AI at the Edge](https://www.wearedevelopers.com/videos/630-trends-challenges-and-best-practices-for-ai-at-the-edge) ## Related Articles - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [Stephan Gillich - Bringing AI Everywhere](https://www.wearedevelopers.com/magazine/489-stephan-gillich-bringing-ai-everywhere)