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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # High-Performance Computing Engineer - **Company:** Bright Vision Technologies - **Location:** Irving, TX, United States (Remote available) - **Experience:** Expert - **Salary:** $100,000.0 - $150,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, C++ (Programming Language), Profiling, Code Review, Nvidia CUDA, Computer Programming, Computer Engineering, Remote Direct Memory Access, Regression Testing, Tensorflow, Scientific Computating, Software Engineering, System Programming, Data Processing, Graphics Processing Unit (GPU), High Performance Computing, Gpu Programming, Information Technology, Free and Open-Source Software, TensorRT - **Published:** July 18, 2026 - **Apply:** https://www.careerjet.com/jobad/us6e806d78f0afbfa7fe16fc045c0eee1a ## 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 GPU programming and performance engineering. * Deep expertise in CUDA C/C++ and GPU programming models. * Strong understanding of modern GPU architectures, memory hierarchies, and execution models. * Hands-on experience profiling and optimizing GPU workloads in production. * Familiarity with NCCL, MPI, and high-performance interconnect technologies. * Experience integrating custom kernels into ML frameworks. * Strong C++ skills and familiarity with modern systems programming practices. * Solid grounding in linear algebra and numerical methods. * Strong communication and collaboration skills with research and engineering teams. Preferred Qualifications * Experience with Triton, CUTLASS, or other GPU kernel authoring frameworks. * Familiarity with TensorRT, FasterTransformer, or vLLM internals. * Exposure to compiler infrastructure such as LLVM or MLIR. * Open-source contributions to GPU or ML performance libraries. * Experience with large-scale distributed training infrastructure. ## Description We are seeking a High Performance Computing Engineer with deep expertise in CUDA programming, GPU architecture, and high-performance computing to design and optimize compute-intensive workloads on modern accelerator hardware. This role focuses on extracting maximum performance from GPU platforms for AI training, inference, scientific computing, and high-throughput data processing workloads. The ideal candidate combines low-level systems mastery with strong software engineering practices, and has a track record of delivering measurable performance improvements on production GPU systems. 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., * Design and implement high-performance CUDA kernels for compute-intensive workloads across AI and HPC use cases. * Profile and optimize GPU code using tools such as Nsight Systems, Nsight Compute, and CUDA profilers. * Tune memory access patterns, occupancy, register usage, and shared memory utilization for peak performance. * Develop highly optimized libraries for linear algebra, attention, and other ML primitives. * Optimize multi-GPU and multi-node training using NCCL, RDMA, and high-performance networking. * Implement custom operators and fused kernels in PyTorch, JAX, or Triton. * Collaborate with ML engineers to identify performance bottlenecks in training and inference pipelines. * Develop benchmarks and regression tests to safeguard performance over time. * Evaluate new GPU architectures and feature sets, and advise on adoption strategy. * Contribute to compiler-level optimizations for tensor programs where appropriate, working at the boundary between ML frameworks and underlying accelerator codegen to unlock performance not reachable through framework-level tuning alone. * Optimize memory hierarchy usage across HBM, L2, shared memory, and registers. * Implement mixed-precision and quantized compute paths that maximize accelerator throughput while preserving numerical fidelity within bounds acceptable for the target workloads. * Document performance characteristics, design decisions, and tuning playbooks for internal teams. * Stay current with GPU architecture, CUDA evolution, and emerging accelerator technologies. ## 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) - [Accelerating Python on GPUs](https://www.wearedevelopers.com/videos/859-accelerating-python-on-gpus) - [Profiling Symfony & PHP apps with Blackfire](https://www.wearedevelopers.com/videos/265-profiling-symfony-php-apps-with-blackfire) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Accelerating Python on GPUs](https://www.wearedevelopers.com/videos/1521-accelerating-python-on-gpus) - [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 - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [7 Cloud Computing Trends Coming in 2025 for Developers](https://www.wearedevelopers.com/magazine/412-7-cloud-computing-trends-coming-in-2025-for-developers) - [Got AI ideas but no money? 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