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Generative Adversarial Network Jobs (NOW HIRING)

Orion Innovation

Senior QA Analyst / Tester

... network of member firms, and collaborate with defensive and development teams ... Key accountabilities % of time Accountability * 10 Design and execute comprehensive adversarial ...

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Generative Adversarial Network information

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$22K

$106.6K

$162.5K

How much do generative adversarial network jobs pay per year?

As of Sep 9, 2026, the average yearly pay for generative adversarial network in the United States is $106,570.00, according to ZipRecruiter salary data. Most workers in this role earn between $80,500.00 and $128,000.00 per year, depending on experience, location, and employer.

What is a generative adversarial network?

A Generative Adversarial Network (GAN) is a type of machine learning framework used for generating new data samples that resemble a given dataset. It consists of two neural networks, called the generator and the discriminator, which compete against each other. The generator creates fake data, while the discriminator tries to distinguish between real and fake data. Through this adversarial process, GANs can learn to produce highly realistic images, audio, and other types of data. They are widely used in applications like image synthesis, style transfer, and data augmentation.

What are the key skills and qualifications needed to thrive as a generative adversarial network engineer?

To thrive as a Generative Adversarial Network (GAN) Engineer, you need a solid background in machine learning, deep learning, and mathematics, typically supported by a degree in computer science or a related field. Familiarity with frameworks like TensorFlow or PyTorch, and experience with GPU computing and model optimization, are crucial technical tools. Strong problem-solving, creativity, and teamwork skills help you develop novel architectures and overcome research challenges. These abilities are essential to drive innovation and create effective, high-performing GAN models in a rapidly evolving AI landscape.

What are typical challenges faced by professionals working with generative adversarial networks in research or industry?

Professionals working with GANs often encounter challenges such as ensuring stable training, preventing mode collapse, and fine-tuning hyperparameters. Training GANs requires balancing the generator and discriminator networks, which can be technically complex and computationally intensive. In a team setting, collaboration with data scientists, machine learning engineers, and domain experts is common to iterate on model architectures and evaluate generated outputs. Additionally, keeping up with rapidly evolving research and tools is essential for ongoing success in this field.

What is the difference between Generative Adversarial Network vs Data Scientist?

AspectGenerative Adversarial NetworkData Scientist
Required credentialsAdvanced knowledge of machine learning, deep learning, programming (Python, TensorFlow)Statistics, programming, data analysis, often a degree in data science or related fields
Work environmentResearch labs, AI development teams, tech companiesBusiness environments, analytics teams, consulting firms
Industry usageAI research, image/video synthesis, data augmentationData analysis, predictive modeling, business insights

While Generative Adversarial Networks focus on creating realistic data through deep learning models, Data Scientists analyze and interpret data to inform business decisions. Both roles require strong technical skills but serve different purposes within the AI and data ecosystem.

What other helpful pages are available for Generative Adversarial Network?

Other pages related to Generative Adversarial Network:

Infographic showing various Generative Adversarial Network job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 81% Full Time, 11% Part Time, and 6% Contract. Highlights an 90% Physical, 2% Hybrid, and 8% Remote job distribution, with an average salary of $106,570 per year, or $51.2 per hour.

Postdoctoral Research Associate (Electrical and Computer Engineering)

Tucson, AZ

University of Arizona
Colleges, Universities, and Professional Schools • 10K+ employees
Learn more about University of Arizonaexternal

Full-time, Part-time

Medical, Dental, Vision, Life, PTO

Re-posted 26 days ago

University Of Arizona rating

7.4

Company rating: 7.4 out of 10

Based on 69 frontline employees who took The Breakroom Quiz

339th of 633 rated colleges and universities

Job description

Postdoctoral Research Associate (Electrical and Computer Engineering)

The Electrical and Computer Engineering (ECE) Department at the University of Arizona is seeking a qualified and highly motivated Postdoctoral Research Associate with background on AI and machine learning for wireless networking and communications. The successful candidate will work under the direction of Dr. Marwan Krunz, Director of the Wireless Communications and Networking (WICON) Lab, at the University of Arizona. The laboratory is actively involved in various projects related to wireless communications, networking, and security, with particular emphasis on resource allocation strategies, distributed protocol design, and security. The incumbent will contribute to various projects on NextGen/5G systems.

The laboratory has been involved in projects related to 5G cellular systems, mmWave protocols (beamforming, initial access, phased antenna arrays, wireless backhauling, etc.), cognitive/agile radios, dynamic and shared spectrum access, harmonious coexistence of heterogeneous wireless systems, full-duplex transmissions, ultra-low-latency mobile edge computing (MEC), network slicing, physical-layer wireless security, satellite communications, MIMO systems, energy management in wireless sensor networks, and streaming over wireless links. We invite qualified candidates to join our group and participate in cutting-edge research related to these topics.

This position is eligible for J-1 visa sponsorship. Outstanding U of A benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; U of A/ASU/NAU tuition reduction for the employee and qualified family members; access to U of A recreation and cultural activities; and more! The University of Arizona has been recognized for our innovative work-life programs.

Duties & Responsibilities:

  • Conduct research on generative AI/ML, including application of generative adversarial and diffusion models for synthesis of 5G/6G coverage maps, channel characterization, medical imaging, and RF signal synthesis.
  • Study new reinforcement learning and DQN designs for beam tracking and management in mobile systems.
  • Investigate spectrum sharing over unlicensed and licensed bands.
  • Study the coexistence between active and passive systems.
  • Interact with industry partners, participate in and support various projects within the WISPER center.
  • Attend WISPER meetings.
  • Foster collaborations within the Department, with other units across the University, and with team members at other institutes.
  • Present research at national and international conferences.
  • Prepare manuscripts for publication in peer-reviewed journals.
  • Participate in grant writing, including generating preliminary data, submitting grant applications, and preparing progress reports.
  • Team up with other members of the WICON group to write joint papers for presentation at top-tier conferences and publication in high-quality archived journals.
  • Assist Dr. Krunz with mentoring graduate students.
  • Additional duties as assigned.

Knowledge, Skills, and Abilities:

  • Expert knowledge of AI and ML techniques, including but not limited to GANs, autoencoders, and diffusion models.
  • Some knowledge of wireless communications systems and Physical-layer security, including RF signal classification and wireless protocols (5G, LTE, and Wi-Fi).
  • Strong analysis skills, research, and technical writing skills.
  • Ability to communicate professionally in a clear, concise manner orally and in writing.

Minimum Qualifications:

  • PhD degree in Electrical Engineering, Electrical and Computer Engineering, Computer Science, or a related field of study.
  • Selected applicant must have PhD conferred upon hire.

Preferred Qualifications:

  • Experience with wireless simulation tools (e.g., LabView, Matlab Toolboxes).
  • Experience with RF experimentations (e.g., USRP radios).
  • Experience with 5G cellular systems, LTE, and Wi-Fi protocols.
  • Experience with proposal writing and grant reporting.

FLSA - Exempt

Full Time/Part Time - Full Time

Number of Hours Worked per Week - 40

Job FTE - 1.0

Work Calendar - Fiscal

Job Category - Research

Benefits Eligible - Yes - Full Benefits

Rate of Pay - NIH Postdoc Salary Scale, Depends on Experience

Compensation Type - salary at 1.0 full-time equivalency (FTE)

Type of criminal background check required: - Name-based criminal background check (non-security sensitive)

Number of Vacancies - 1

Target Hire Date - 1/26/2026

Contact Information for Candidates - Dr. Marwan Krunz

Open Date - 12/5/2025

Open Until Filled - Yes

Documents Needed to Apply - Curriculum Vitae (CV) and Cover Letter

Special Instructions to Applicant - Cover Letter: Should clearly indicate how your skills and professional employment experience meet the Minimum and the Preferred qualifications (if applicable).

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