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# AI Infrastructure Engineer

- **Company:** Bright Vision Technologies
- **Location:** 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, Software Debugging, Distributed Computing Environment, Distributed Systems, Memory Management, Python (Programming Language), Performance Tuning, AI Infrastructure, Graphics Processing Unit (GPU), Large Language Models, Deep Learning, Parallel Computation, Information Technology, Data Analytics, Machine Learning Operations, TensorRT
- **Published:** July 29, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=7ef963989a63378f

## About the Role

a clear communication style, and a track record of shipping meaningful work that holds up well in production., * Bachelor's or master's degree in computer science, Computer Engineering, or 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

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 an impact on production of 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


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