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Enterprise Sales Engineer - Rockies
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Remote, United States
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Contact Center Technology Engineer
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xIL Plant Modeling and Integration Engineer
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Design and Release Engineer - Air Intake System Product
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Product Engineering – Lead System Integrator
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Readiness Coordinator - PA Work Management - New Castle Service Building
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Staff DevOps Engineer
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State College, PA
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Staff DevOps Engineer
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State College, PA
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Lead Principal Application Software Engineer- Forward Deployed Engineer
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Imaging Service Engineer I - Minnesota
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Automation Engineer - Advanced Manufacturing
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Software Test Engineer
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Electrical Design Engineer II
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Entry Level Electrical Engineer
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Bright Vision Technologies

Parallel Computing Engineer

Remote, United States

$130,000 to $180,000 USD / year

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Job details

Pay: $130,000 to $180,000 USD / year
Apply by: Jan 31, 2027
Posting language: English
Posted: Nov 2, 2026

Original job description

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 jaya@bvteck.com or contact us at (908) 505-3545. Learn more about Bright Vision Technologies at www.bvteck.com. 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.

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