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Gan Jobs (NOW HIRING)

Navitas Semiconductor

Initiates the internal assembly design of the GaN device, BOM & Process flow in a target package footprint * Prepared preliminary Build diagram and Package Outline Drawings. * Conducts risk ...

Gan & Lee Pharmaceuticals

The position will work with Gan & Lee clinical program lead(s) to provide the scientific and clinical leadership and inputs in meeting the development objectives for the program.Education and ...

Qorvo, Inc.

Sr Principal GaN Scientist

Richardson, TX · On-site

Extensive hands-on experience in developing and releasing advanced device technologies, particularly GaN , with complementary expertise in GaAs , InP , and other compound semiconductors. * Cross ...

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Gan information

What is a GAN?

GANs, or Generative Adversarial Networks, are a class of machine learning frameworks where two neural networks, called the generator and discriminator, are trained together in a competitive setting. The generator creates data samples that mimic real data, while the discriminator evaluates whether samples are real or generated. This process helps GANs learn to produce highly realistic data, such as images, videos, or audio, making them popular in applications like image synthesis, data augmentation, and creative AI tasks. GANs have become an important tool in artificial intelligence and deep learning research.

What are some common challenges faced by GAN researchers or engineers in industry roles?

Professionals working with GANs often encounter challenges such as training instability, mode collapse, and the need for large, high-quality datasets. GANs require careful tuning of hyperparameters and regular troubleshooting to balance the generator and discriminator, which can be time-consuming. Collaboration with data scientists, software engineers, and domain experts is typical to refine models and deploy them in production, and staying updated with the latest research is key to overcoming technical hurdles.

What are the key skills and qualifications needed to thrive as a GAN engineer, and why are they important?

To thrive as a GAN Engineer, you need a strong background in machine learning, deep learning, and mathematics, typically supported by a degree in computer science or a related field. Expertise in frameworks such as TensorFlow or PyTorch, and experience with programming languages like Python, are essential, along with familiarity with GAN architectures. Creativity, strong problem-solving skills, and effective communication help distinguish top performers in this role. These abilities are critical for developing, optimizing, and explaining innovative generative models that advance AI capabilities.

What is the difference between Gan vs Machine Learning Engineer?

AspectGanMachine Learning Engineer
Required CredentialsTypically a degree in computer science, AI, or related fields; experience with deep learning frameworksSimilar credentials; often requires knowledge of algorithms, programming, and data analysis
Work EnvironmentResearch labs, AI startups, tech companies focusing on generative modelsTech companies, data-driven organizations, AI departments across industries
Industry UsagePrimarily in AI research, generative modeling, and creative applicationsBroader industry applications including predictive modeling, data analysis, and automation

Gan (Generative Adversarial Network) specialists focus on developing generative models for creating new data, images, or content. Machine Learning Engineers work on designing, implementing, and optimizing various machine learning models across multiple applications. While both roles require a strong background in AI and programming, Gans are more specialized in generative modeling, whereas Machine Learning Engineers have a broader scope in deploying and maintaining machine learning solutions.

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The top searched job categories for Gan jobs are:

Gan jobs near you
Infographic showing various Gan job openings in the United States as of August 2026, with employment types broken down into 98% Full Time, and 2% Part Time. Highlights an 93% Physical, 3% Hybrid, and 4% Remote job distribution.

GaN Package Development Engineer

Navitas Semiconductor

On-site

Full-time

Posted 21 days ago

Job description

Key Responsibilities and DutiesDesign & Risk Assessment
  • Initiates the internal assembly design of the GaN device, BOM & Process flow in a target package footprint
  • Prepared preliminary Build diagram and Package Outline Drawings.
  • Conducts risk assessment and identification of mitigating actions to eliminate or reduce the risks. This will be in collaboration with OSAT development team and Navitas Operations/NPI teams
  • Works with Simulation engineer for thermal and thermo-mechanical stress simulations as part of risk assessment of new designs.
  • Lead cross-functional team reviews to finalize proposed design
  • Defines new assembly package layout rules as reference for future designs
Package Development
  • Drives all assembly validation activities to freeze Design-, BOM- and Process-of-records
  • Performs Learning Cycle builds to validate design and material selection versus target performance
  • Works with Rel team to execute Look-ahead reliability evaluations and post-stress construction analysis to assess package robustness
  • Oversees the assembly of Final POR Qualification Lots at OSAT up to delivery of samples for Reliability testing
  • Compiles and provides all relevant documentation under Package Engineering deliverables as per Navitas New Product Development Process
Customer Support
  • Generates Engineering build requests for customer samples requested by BU
  • Monitors assembly CTQs and yield performance of customer sample lots for CPK and yield analysis
  • Generates Application notes for new packages to provide guidance for optimum board mount conditions of Navitas products.
Required Qualifications
  • Bachelors degree or MS degree in Semiconductor Engineering, Materials Science, Chemical Engineering or a related technical field.
  • At least 15 years experience in Package design and development
  • Strong understanding of power semiconductor device physics and manufacturing processes.
  • Experience with SiC and/or GaN technologies preferred.
  • Deep working knowledge of SPC, DOE, Cpk/Ppk analysis, FMEA, control plans, root-cause analysis, and yield analytics.
  • Proven experience leading engineering projects and mentoring technical teams.
  • Excellent communication skills with the ability to collaborate effectively across global, cross-functional organizations.
Preferred Qualifications
  • Package design using 2D CAD
  • Understanding of FEA simulation techniques, reliability qualification standards, failure-analysis methodologies, and process excursion management.
  • Experience in new assembly technologies for advanced power devices (ie flipchip, clip attach, Ag sinter, etc)
  • Experience in leading or coordinating geographically distributed engineering teams.

Apply