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Deep Learning Accelerator Jobs (NOW HIRING)

Study state-of-the-art computer vision and deep learning models and define efficient mappings onto mobile AI accelerators and heterogeneous compute platforms. * Drive architecture development for ...

The Deep Learning Inference team develops and optimizes open-source frameworks that make AI ... Our work enables developers worldwide to harness NVIDIA accelerators for real-time inference at ...

The Deep Learning Inference team develops and optimizes open-source frameworks that make AI ... Our work enables developers worldwide to harness NVIDIA accelerators for real-time inference at ...

The Deep Learning Inference team develops and optimizes open-source frameworks that make AI ... Our work enables developers worldwide to harness NVIDIA accelerators for real-time inference at ...

The Deep Learning Inference team develops and optimizes open-source frameworks that make AI ... Our work enables developers worldwide to harness NVIDIA accelerators for real-time inference at ...

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Deep Learning Accelerator information

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$11K

$83.9K

$140K

How much do deep learning accelerator jobs pay per year?

As of Sep 10, 2026, the average yearly pay for deep learning accelerator in the United States is $83,885.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,000.00 and $139,000.00 per year, depending on experience, location, and employer.

What is a deep learning accelerator?

Deep Learning Accelerators are specialized hardware or systems designed to speed up the processing and training of deep learning algorithms, such as neural networks. They are optimized for the heavy computational demands of tasks like image recognition, natural language processing, and other AI applications. Examples include Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), and custom-designed chips like Application-Specific Integrated Circuits (ASICs) and Field-Programmable Gate Arrays (FPGAs). These accelerators enable faster data processing, lower power consumption, and improved efficiency compared to general-purpose CPUs. As AI applications grow, the use of deep learning accelerators is becoming increasingly important in both research and industry.

What skills and qualifications are needed to thrive as a deep learning accelerator engineer?

To thrive as a Deep Learning Accelerator Engineer, you need a strong background in computer engineering, digital design, and machine learning, typically supported by a degree in computer science or electrical engineering. Experience with hardware description languages (such as Verilog or VHDL), FPGA/ASIC toolchains, and familiarity with deep learning frameworks like TensorFlow or PyTorch is essential. Problem-solving, teamwork, and effective communication are crucial soft skills for collaborating with cross-functional teams and translating algorithmic requirements into efficient hardware solutions. These skills are vital to designing high-performance, energy-efficient hardware accelerators that advance AI capabilities and meet industry demands.

What are the main challenges faced when optimizing deep learning models for hardware accelerators?

One of the primary challenges in this role is bridging the gap between deep learning model requirements and the constraints of specialized hardware, such as GPUs, TPUs, or custom ASICs. This often involves model quantization, memory optimization, and adapting algorithms to exploit hardware parallelism while maintaining accuracy and efficiency. Collaboration with both hardware engineers and software developers is essential to ensure models run efficiently on target platforms, and staying current with evolving accelerator architectures is key to long-term success.

What is the difference between Deep Learning Accelerator vs Machine Learning Engineer?

AspectDeep Learning AcceleratorMachine Learning Engineer
Required CredentialsKnowledge of hardware design, FPGA/ASIC programming, deep learning frameworksDegree in Computer Science, Data Science, or related fields; experience with ML frameworks
Work EnvironmentHardware development labs, embedded systems, AI hardware companiesSoftware development environments, tech companies, research labs
Industry UsageAI hardware manufacturing, embedded AI solutionsAI/ML software development, data analysis, model deployment
Search & Comparison IntentFocus on hardware acceleration, AI hardware designFocus on software development, model building

Deep Learning Accelerators specialize in hardware design and optimization for AI workloads, working closely with hardware and embedded systems. Machine Learning Engineers develop and deploy ML models primarily through software, focusing on algorithms and data. While both roles involve AI, their core skills, work environments, and industry applications differ significantly.

What cities are hiring for Deep Learning Accelerator jobs?

Cities with the most Deep Learning Accelerator job openings:

What states have the most Deep Learning Accelerator jobs?

States with the most job openings for Deep Learning Accelerator jobs include:

What are popular job titles related to Deep Learning Accelerator jobs?

For Deep Learning Accelerator jobs, the most frequently searched job titles are:

Infographic showing various Deep Learning Accelerator job openings in the United States as of June 2026, with employment types broken down into 4% Internship, 8% As Needed, 80% Full Time, 4% Part Time, and 4% Summer. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $83,885 per year, or $40.3 per hour.

Computer Vision System Engineer San Diego, California, United States of America Video Systems, HW Ar

San Diego, CA • On-site

Qualcomm
Technology, Communication and Media • 10K+ employees

Other

Posted 20 days ago


Key responsibilities

  • Study state-of-the-art computer vision and deep learning models and define efficient mappings onto mobile AI accelerators and heterogeneous compute platforms.

  • Drive architecture development for hardware-aware deep learning engines, including support for neural network operators, dataflows, tensor processing pipelines, quantization, and memory hierarchies.

  • Analyze neural network workloads and identify architectural enhancements to improve performance, power efficiency, memory bandwidth utilization, and silicon area.


Qualcomm rating

8.8

Company rating: 8.8 out of 10

Based on 11 frontline employees who took The Breakroom Quiz


Job description

Company

Qualcomm Technologies, Inc.

Job Area

Engineering Group, Engineering Group > Video Systems, HW Architecture

General Summary

Qualcomm's computer vision system design Group is seeking candidates for its Mobile Computer Vision and AI Systems Architecture Team. The team develops next-generation mobile computer vision and deep learning solutions for imaging, perception, scene understanding, segmentation, tracking, computational photography, and AI-powered experiences.

We are seeking candidates with strong expertise in hardware-aware algorithm design, deep learning engine architecture, system architecture, HW/SW co-design, and accelerator development for mobile computer vision and AI workloads. The ideal candidate will possess deep knowledge of computer vision and neural network algorithms and practical experience translating them into power-efficient, real-time implementations on heterogeneous mobile platforms consisting of CPUs, GPUs, DSPs, NPUs, and dedicated AI accelerators. The candidate is expected to drive architecture definition for next-generation deep learning engines and computer vision accelerators, including compute, memory, dataflow, scheduling, quantization, and HW/SW partitioning strategies that maximize performance-per-watt.

Principal Duties and Responsibilities
  • Study state-of-the-art computer vision and deep learning models and define efficient mappings onto mobile AI accelerators and heterogeneous compute platforms.
  • Drive architecture development for hardware-aware deep learning engines, including support for emerging neural network operators, dataflows, tensor processing pipelines, quantization techniques, and memory hierarchies.
  • Analyze neural network workloads and identify architectural enhancements required to improve performance, power efficiency, memory bandwidth utilization, and silicon area.
  • Define HW/SW partitioning strategies across CPUs, GPUs, DSPs, NPUs, and dedicated accelerators.
  • Develop workload characterization methodologies and performance models for computer vision and AI applications.
  • Collaborate with hardware architects and designers to define next-generation AI engine features and capabilities based on evolving computer vision workloads.
  • Drive top-down architecture exploration from algorithm requirements through hardware implementation, including performance, power, thermal, and area projections.
Minimum Qualifications
  • Bachelor's degree in Computer or Electrical Engineering, Computer Science, or related field and 2+ years of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience.
  • Master's degree in Computer or Electrical Engineering, Computer Science, or related field and 1+ year of Software Engineering, Hardware Engineering, Systems Engineering, or related work experience.
  • PhD in Computer or Electrical Engineering, Computer Science, or related field.
Preferred Qualifications
  • Multiple years of experience developing mobile computer vision and AI systems.
  • Deep knowledge of modern neural network architectures, including CNNs, Transformers, Vision Transformers (ViTs), multi-modal perception systems, and hardware-aware model optimization techniques.
  • Experience architecting and optimizing deep learning engines (DLEs) or AI accelerators for computer vision workloads.
Additional Requirements
  • Strong understanding of mobile computer vision applications, including: Object Detection and Tracking, Segmentation, Scene Understanding, Image Enhancement and Computational Photography, Motion Estimation and Neural Network-based Vision Systems.
  • Expertise in system architecture and HW/SW partitioning for computer vision and AI workloads.
  • Strong understanding of deep learning accelerator architectures, including Tensor processing, optimization, compute and neural network bottlenecks.
  • Experience defining real-time hardware architectures for computer vision and deep learning workloads.
  • Experience evaluating throughput, latency, power, memory bandwidth, and silicon area trade-offs.
  • Strong programming skills in C/C++ and Python with hands-on experience in algorithm prototyping and performance analysis.
Equal Opportunity Employer Statement

Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail disability-accomodations@qualcomm.com or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).

EEO Employer

Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification.

Pay range and Other Compensation & Benefits

$122,500.00 - $213,200.00

The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer – and you can review more details about our US benefits at this link.

Policy Statement

Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.

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About Qualcomm

Sourced by ZipRecruiter

Qualcomm is enabling a world where everyone and everything can be intelligently connected. You interact with products and technologies made possible by Qualcomm every day, including 5G-enabled smartphones that double as pro-level cameras and gaming devices, smarter vehicles and cities, and the technology behind the smart, connected factories that manufactured your latest purchase. Our powerful connectivity solutions keep you connected—even in remote areas. Qualcomm 5G and AI innovations are the power behind the connected intelligent edge. You’ll find our technologies behind and inside the innovations that deliver significant value across multiple industries and to billions of people every day.

Industry

Technology, communication and media

Company size

10,000+ Employees

Headquarters location

San Diego, CA, US

Year founded

1985