并行计算工程师
Parallel Computing Engineer
Bright Vision Technologies 是一家技术咨询和软件开发公司,为美国各地提供云、人工智能、数据和企业解决方案。这是一次加入一家知名且受人尊敬的组织的绝佳机会,提供巨大的职业发展潜力。
职位名称
并行计算工程师
地点:100% 远程(美国)
职位类型:全职,直接 W2
薪资范围:每年 13 万至 18 万美元
经验要求:10 年以上
赞助:美国公民、绿卡持有者、EAD 持有者以及 H-1B 转移候选人欢迎申请。我们无法为该职位提供新的 H-1B 签证申请。
职位简介
Bright Vision Technologies 正在寻找一位拥有 10 年以上高性能计算(HPC)、GPU 编程和并行计算经验的资深并行计算工程师,以优化人工智能、机器学习和科学计算工作负载。理想的候选人应具备 CUDA、GPU 架构、分布式计算、性能优化和大规模 AI 基础设施的深厚专业知识,并在为企业和研究环境设计高性能计算解决方案方面有成功经验。
主要职责
- 设计、开发和优化用于人工智能、深度学习和科学计算应用的高性能 CUDA 内核。
- 使用 NVIDIA Nsight Systems、Nsight Compute、CUDA Profiler 和相关性能分析工具分析、剖析和优化 GPU 工作负载。
- 优化 GPU 内存管理、内核执行、多 GPU 扩展和分布式计算性能。
- 使用 NCCL、MPI、CUDA-aware 通信库和高性能网络技术设计可扩展的分布式训练和推理架构。
- 为 PyTorch、JAX、Triton、TensorFlow 或类似的人工智能框架开发自定义 GPU 操作符和优化内核。
- 提升大型语言模型(LLMs)、深度学习和高性能人工智能工作负载的训练和推理性能。
- 与人工智能研究人员、机器学习工程师和软件架构师合作,加速生产环境的人工智能应用。
- 构建自动化基准测试框架、性能回归测试和优化流程。
- 评估新兴 GPU 技术、编程模型和加速器架构,以提高计算效率。
- 指导工程师,提供 GPU 优化、HPC 架构和并行编程最佳实践方面的技术领导力。
查看英文原文
Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.
Job Title
Parallel Computing Engineer
Location: 100% Remote (U.S.)
Position Type: Full-time, Direct W2
Salary Range: $130,000–$180,000 Annually
Experience Required:10+ Years
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.
Job Summary
Bright Vision Technologies is seeking a highly experienced Parallel Computing Engineer with 10+ years of experience in High-Performance Computing (HPC), GPU programming, and parallel computing to optimize AI, machine learning, and scientific computing workloads. The ideal candidate will possess deep expertise in CUDA, GPU architecture, distributed computing, performance optimization, and large-scale AI infrastructure, with a proven track record of designing high-performance computing solutions for enterprise and research environments.
Key Responsibilities
- Design, develop, and optimize high-performance CUDA kernels for AI, deep learning, and scientific computing applications.
- Analyze, profile, and optimize GPU workloads using NVIDIA Nsight Systems, Nsight Compute, CUDA Profiler, and related performance analysis tools.
- Optimize GPU memory management, kernel execution, multi-GPU scaling, and distributed computing performance.
- Design scalable distributed training and inference architectures using NCCL, MPI, CUDA-aware communication libraries, and high-performance networking technologies.
- Develop custom GPU operators and optimized kernels for PyTorch, JAX, Triton, TensorFlow, or similar AI frameworks.
- Improve training and inference performance for large language models (LLMs), deep learning, and high-performance AI workloads.
- Collaborate with AI researchers, ML engineers, and software architects to accelerate production AI applications.
- Build automated benchmarking frameworks, performance regression testing, and optimization pipelines.
- Evaluate emerging GPU technologies, programming models, and accelerator architectures to improve computational efficiency.
- Mentor engineers and provide technical leadership in GPU optimization, HPC architecture, and parallel programming best practices.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical discipline.
- 10+ years of professional experience in GPU programming, High-Performance Computing (HPC), or parallel computing.
- Expert-level proficiency in CUDA C/C++, GPU architecture, and massively parallel programming techniques.
- Extensive experience with NCCL, MPI, CUDA-aware MPI, and distributed GPU communication frameworks.
- Strong understanding of GPU memory hierarchy, kernel optimization, occupancy tuning, and performance analysis.
- Hands-on experience integrating custom GPU kernels into PyTorch, TensorFlow, JAX, Triton, or other machine learning frameworks.
- Strong C/C++ programming skills with expertise in debugging, profiling, and performance optimization.
- Experience developing scalable AI or HPC solutions on cloud platforms or large GPU clusters.
- Excellent analytical, communication, collaboration, and technical leadership skills.
Preferred Qualifications
- Experience with Triton, CUTLASS, TensorRT, FasterTransformer, vLLM, DeepSpeed, or similar GPU optimization frameworks.
- Knowledge of LLVM, MLIR, compiler optimization techniques, or code generation technologies.
- Experience with large-scale distributed AI training, model parallelism, pipeline parallelism, and inference optimization.
- Familiarity with cloud-based GPU infrastructure on AWS, Microsoft Azure, or Google Cloud Platform (GCP).
- Contributions to open-source GPU libraries, research publications, patents, or technical presentations.
- Experience with emerging accelerator technologies such as AMD ROCm, Intel
How to Apply
Would you like to know more about this opportunity? For immediate consideration, please send your resume to or contact us at (908) 505-3545. Learn more about Bright Vision Technologies at .
Bright Vision Technologies is an Equal Opportunity Employer.Equal Employment Opportunity (EEO) Statement
Bright Vision Technologies (BV Teck) is committed to equal employment opportunity (EEO) for all employees and applicants without regard to race, color, religion, sex, sexual orientation, gender identity or expression, national origin, age, genetic information, disability, veteran status, or any other protected status as defined by applicable federal, state, or local laws. This commitment extends to all aspects of employment, including recruitment, hiring, training, compensation, promotion, transfer, leaves of absence, termination, layoffs, and recall.
BV Teck expressly prohibits any form of workplace harassment or discrimination. Any improper interference with employees' ability to perform their job duties may result in disciplinary action up to and including termination of employment.
Originally posted on Himalayas