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Neural Processing Unit Engineer Jobs (NOW HIRING)

quadric, Inc

AI Kernel Engineer

Burlingame, CA · On-site

$110K - $270K/yr

Quadric has created an innovative general purpose neural processing unit (GPNPU) architecture ... Role The AI Kernel Engineer in Quadric plays the key role to enable a large number of AI kernels ...

quadric, Inc

Forward Deployed Engineer

$175K - $225K/yr

About Quadric Quadric is redefining edge AI with the industry's first General Purpose Neural Processing Unit (GPNPU), enabling developers to run both neural network inference and conventional C ...

Quadric

Forward Deployed Engineer

Burlingame, CA · On-site

$175K - $225K/yr

About Quadric Quadric is redefining edge AI with the industry's first General Purpose Neural Processing Unit (GPNPU), enabling developers to run both neural network inference and conventional C ...

quadric, Inc

AI Kernel Engineer

Burlingame, CA · On-site

$110K - $270K/yr

Quadric has created an innovative general purpose neural processing unit (GPNPU) architecture ... Role The AI Kernel Engineer in Quadric plays the key role to enable a large number of AI kernels ...

quadric, Inc

Staff SoC RTL Engineer

Burlingame, CA · On-site

$175K - $230K/yr

About Quadric Quadric is redefining edge AI with the industry's first General Purpose Neural Processing Unit (GPNPU), enabling developers to run both neural network inference and conventional C ...

quadric, Inc

Staff SoC RTL Engineer

Burlingame, CA · On-site

$175K - $230K/yr

About Quadric Quadric is redefining edge AI with the industry's first General Purpose Neural Processing Unit (GPNPU), enabling developers to run both neural network inference and conventional C ...

quadric.io, Inc

Staff SoC RTL Engineer

Burlingame, CA · On-site

$175K - $230K/yr

About Quadric Quadric is redefining edge AI with the industry's first General Purpose Neural Processing Unit (GPNPU), enabling developers to run both neural network inference and conventional C ...

quadric, Inc

Staff SoC RTL Engineer

Burlingame, CA · On-site

$175K - $230K/yr

About Quadric Quadric is redefining edge AI with the industry's first General Purpose Neural Processing Unit (GPNPU), enabling developers to run both neural network inference and conventional C ...

quadric, Inc

AI Kernel Engineer

Burlingame, CA · On-site

$110K - $270K/yr

Quadric has created an innovative general purpose neural processing unit (GPNPU) architecture ... Role The AI Kernel Engineer in Quadric plays the key role to enable a large number of AI kernels ...

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Neural Processing Unit Engineer information

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$49.5K

$113.5K

How much do neural processing unit engineer jobs pay per year?

As of Sep 10, 2026, the average yearly pay for neural processing unit engineer in the United States is $108,847.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,000.00 and $113,000.00 per year, depending on experience, location, and employer.

What is a neural processing unit engineer?

A Neural Processing Unit (NPU) Engineer is a specialized hardware or software engineer who designs, develops, and optimizes processors specifically built to accelerate artificial intelligence (AI) and machine learning tasks. These engineers work on creating efficient NPU architectures, writing low-level code, and integrating NPUs into various computing systems such as smartphones, edge devices, and data centers. Their main goal is to maximize the performance and energy efficiency of AI workloads, such as neural network inference and training, by leveraging dedicated hardware. NPU Engineers often collaborate with data scientists, software developers, and hardware teams to ensure seamless deployment of AI models.

What are the key skills and qualifications needed to thrive as a neural processing unit engineer?

To thrive as a Neural Processing Unit Engineer, you need a solid background in computer engineering, digital circuit design, and deep learning algorithms, often supported by a relevant degree in electrical engineering or computer science. Familiarity with hardware description languages (HDL), simulation tools like ModelSim, and frameworks such as TensorFlow or PyTorch is typically required. Strong problem-solving skills, attention to detail, and effective teamwork set top performers apart in this role. These competencies are crucial for designing efficient NPUs that accelerate AI workloads and meet evolving performance and energy efficiency demands.

What are some common challenges faced by neural processing unit engineers when optimizing hardware for AI workloads?

Neural Processing Unit (NPU) Engineers often encounter challenges in balancing performance, power efficiency, and scalability while designing hardware for AI workloads. Achieving low latency and high throughput for diverse neural network models requires innovative architecture and close collaboration with software teams. Additionally, NPUs must be flexible enough to support emerging AI algorithms, which means engineers need to stay current with rapid advancements in machine learning. Overcoming these challenges typically involves extensive simulation, benchmarking, and iterative hardware-software co-optimization.

What is the difference between Neural Processing Unit Engineer vs AI Hardware Engineer?

AspectNeural Processing Unit EngineerAI Hardware Engineer
CredentialsBachelor's or Master's in Electrical Engineering, Computer Engineering, or related fields; experience with hardware design and AI acceleratorsBachelor's or Master's in Electrical Engineering, Computer Engineering, or related fields; focus on hardware development for AI systems
Work EnvironmentDesigning, testing, and optimizing neural processing units in R&D labs or tech companiesDeveloping and integrating AI hardware components in product development or research settings
Industry UsagePrimarily in AI chip design, machine learning hardware accelerationBroader AI hardware development including processors, accelerators, and embedded systems

Neural Processing Unit Engineers focus specifically on designing and optimizing neural processing units for AI applications, while AI Hardware Engineers work on a wider range of AI hardware components. Both roles require similar technical backgrounds but differ in scope and specialization within AI hardware development.

What are popular job titles related to Neural Processing Unit Engineer jobs?

For Neural Processing Unit Engineer jobs, the most frequently searched job titles are:

Infographic showing various Neural Processing Unit Engineer job openings in the United States as of September 2026, with employment types broken down into 2% As Needed, 80% Full Time, 11% Part Time, and 7% Contract. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $108,847 per year, or $52.3 per hour.

Graphic Processing Unit (GPU) Engineer - TS/SCI

Bethesda, MD • On-site

Xcelerate Solutions
Business Management Consulting • 201 - 500 employees
Learn more about Xcelerate Solutionsexternal

$149K - $185K/yr

Full-time

Re-posted 28 days ago

Key responsibilities

  • Design, develop, and optimize GPU architectures and hardware for performance, power efficiency, and feature requirements.

  • Work with operating system developers to ensure smooth GPU integration, compatibility, and performance in Linux-based systems.

  • Develop, optimize, and maintain GPU applications using CUDA or OpenCL, and create tools for debugging, profiling, and performance analysis on Linux platforms.

Job description

Description
Graphics Processing Unit (GPU) Engineer - TS/SCI
Xcelerate Solutions is looking for a highly skilled Graphics Processing Unit (GPU) Engineer with a deep understanding of operating systems, hardware, and extensive knowledge of the GPU industry, particularly in the context of Linux-based systems. As a GPU Engineer, you will play a pivotal role in designing, developing, and optimizing GPUs for various applications, with a strong emphasis on seamless integration with operating systems and hardware. Your expertise will contribute to advancing GPU technology and its efficient utilization in diverse fields.
Location:
Bethesda, MD
Security Clearance:
TS/SCI and willingness to get a Poly.
Primary Responsibilities
  • GPU Architecture and Design: Collaborate with a multidisciplinary team to define, develop, and optimize GPU architectures, ensuring they meet stringent performance, power efficiency, and feature requirements. Leverage industry insights to drive design decisions. Ensure that GPU designs and integrations are not only optimized for Linux but are also adaptable to other operating systems.
  • Operating System Integration: Work closely with operating system developers to ensure smooth GPU integration with Linux-based systems. Optimize GPU drivers for compatibility, performance, and reliability in a Linux environment. Provide regular maintenance and updates to ensure continued compatibility.
  • Hardware Expertise: Contribute to the design and development of GPU hardware, providing insights into hardware architecture to ensure efficient interaction with software components. Maintain and update hardware designs as needed.
  • CUDA (Compute Unified Device Architecture) /OpenCL (Open Computing Language) Programming: Develop and optimize applications using CUDA or OpenCL, harnessing the full potential of GPU hardware for parallel processing, high-performance computing, and machine learning on Linux platforms. Maintain and update software for optimal performance.
  • Performance Analysis: Analyze GPU performance, identify bottlenecks, and develop strategies to enhance performance across various applications in Linux, addressing both hardware and software considerations. Regularly monitor and improve performance.
  • GPU Tooling: Create and maintain debugging tools, profiling utilities, and performance analysis software tailored for Linux systems to facilitate efficient GPU development and troubleshooting. Keep tools up-to-date and functional.
  • Power Efficiency: Work on power management techniques to optimize GPU power consumption, ensuring efficient operation on both mobile and desktop Linux platforms. Continuously assess and enhance power efficiency strategies.
  • Testing and Validation: Design and execute tests to validate GPU performance and functionality on Linux, including stress testing, benchmarking, and debugging to ensure robust operation. Maintain and expand the testing suite.
  • Documentation: Maintain comprehensive technical documentation, including architectural specifications, code documentation, and Linux-specific best practices for GPU development. Keep documentation up-to-date with changes and improvements.
  • Industry Insight: Stay updated on the latest trends, innovations, and competitive landscapes within the GPU industry, contributing to research efforts and proposing Linux-specific approaches to GPU design and optimization. Share regular updates and insights with the team.

Minimum Requirement:
  • Bachelor's or higher degree in Computer Science, Electrical Engineering, or a related field. Additional years of experience may be considered in lieu of a degree.
  • 10+ years of relevant systems engineering experience
  • Proven experience in GPU architecture design, and GPU performance optimization.
  • Expertise in operating system integration for Linux.
  • Strong understanding of computer hardware architecture, particularly as it relates to Linux systems.
  • Knowledge of parallel computing, graphics algorithms, and real-time rendering in Linux environments.
  • Familiarity with GPU debugging tools and profiling software for Linux.
  • Excellent problem-solving skills and the ability to collaborate within a team.
  • Strong communication skills for conveying technical information in a Linux context.
  • Proficiency with scripting languages such as Python or BASH.
  • Proficiency with automation tools such Ansible, Puppet, Salt, Terraform, etc.
  • Candidate must, at a minimum, meet DoD 8570.11- IAT Level II certification requirements (currently Security+ CE, CCNA-Security, GICSP, GSEC, or SSCP along with an appropriate computing environment (CE) certification). An IAT Level III certification would also be acceptable (CASP+, CCNP Security, CISA, CISSP, GCED, GCIH, CCSP).

Preferred Qualifications:
  • Published research or contributions in the GPU industry, especially related to Linux.
  • Experience with machine learning and neural network frameworks on GPUs in Linux.
  • Knowledge of GPU virtualization, cloud computing, and emerging Linux-based technologies in the field.
  • Proficiency in programming languages such as GPU-specific languages.
  • Experience with container technologies (Docker, Kubernetes)
  • Experience with Prometheus/Grafana for monitoring
  • Knowledge of distributed resource scheduling systems [Slurm (preferred), LSF, etc.]
  • Familiarity with CUDA and managing GPU-accelerated computing systems
  • Basic knowledge of deep learning frameworks and algorithms

About Xcelerate Solutions:
Founded in 2009 and headquartered in McLean, VA, Xcelerate Solutions (www.xceleratesolutions.com) is one of America's fastest-growing companies. Xcelerate's culture is defined by our diversified workforce of dynamic and versatile professionals, supported with growth and development opportunities that contribute to individual and company growth. This strong commitment to our employees has been recognized by our inclusion on the Washington Business Journal's "50 Best Places to Work" list as well as being a "Great Place to Work" certified company with a 4.6 star, and a 99% CEO approval Glassdoor rating. Come find out why Xcelerate Solutions is one of the DC Metro top employers!
Xcelerate Solutions is an Equal Employment Opportunity/Affirmative Action Employer. We evaluate qualified applicants without regard to race, color, national origin, religion, age, equal pay, disability, veteran status, sex, sexual orientation, gender identity, genetic information, or expression of another protected characteristic. As part of this commitment to the full inclusion of all qualified individuals, Xcelerate provides reasonable accommodations if needed because of an applicant's or an employee's disability.
Pay Transparency Notice: Xcelerate Solutions will not discharge or in any other manner discriminate against employees or applicants because they have inquired about, discussed, or disclosed their own pay or the pay of another employee or applicant.

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