> Markdown version of [/jobs/ext/1247407-ai-performance-engineer](https://www.wearedevelopers.com/jobs/ext/1247407-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:** Cobb, GA, United States (Remote available) - **Experience:** Expert - **Salary:** $100,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Artificial Neural Networks, C++ (Programming Language), Profiling, Code Review, Computer Engineering, Extract Transform Load (ETL), Software Debugging, Distributed Computing Environment, Distributed Systems, Memory Management, Python (Programming Language), Performance Tuning, Graphics Processing Unit (GPU), Large Language Models, Deep Learning, Parallel Computation, Information Technology, Data Analytics, Machine Learning Operations, TensorRT, Data Pipelines - **Published:** July 12, 2026 - **Apply:** https://www.careerjet.com/jobad/usa0738057d75017fa97b86dd8f63b5543 ## About the Role * 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 This role is part of Bright Vision Technologies' in-house Statement of Work (SOW) engagement. The client, end customer, and employer for this position is Bright Vision Technologies - there is no third-party client, vendor, or implementation partner involved. We do not engage in C2C, 1099, or third-party arrangements for this role. BUT STRICTLY NO C2C/1099/3RD PARTY COMPANIES. ALL OUR ROLES ARE W2 AND NO 3RD PARTY BROKERING PLEASE. Candidates must be willing to work directly as a full-time W2 employee of Bright Vision Technologies and contribute to our in-house SOW deliverables. No new H1B sponsorship is available for this role. However, candidates who are currently on a valid H1B visa and require a transfer are welcome to apply. We will support H1B transfers for qualified candidates. For every role, a technical coding assessment is mandatory. Please apply only if you are confident in your technical abilities and hands-on experience., We are seeking an AI Performance 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., * 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 - [Efficient deployment and inference of GPU-accelerated LLMs​](https://www.wearedevelopers.com/videos/929-efficient-deployment-and-inference-of-gpu-accelerated-llms) - [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) - [Are Code Reviews Worth It? Insights from 16 Years of Review Data](https://www.wearedevelopers.com/videos/1135-are-code-reviews-worth-it-insights-from-16-years-of-review-data) - [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) - [AI That Fits Your Business, Not the Other Way Around](https://www.wearedevelopers.com/videos/100148-ai-that-fits-your-business-not-the-other-way-around) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development) - [Got AI ideas but no money? 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