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Generative Ai Engineer Jobs in California (NOW HIRING)

Abhyanth Solutions

Generative AI Engineer

Fremont, CA · On-site

Generative AI Engineer - Fremont, CA Location: Fremont, CA | Job Type: Full-Time Required Qualifications * 10+ years of professional full-stack software engineering experience. * Proven experience ...

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Helix AI Engineer, Generative AI

San Jose, CA · On-site

They are seeking a Helix AI Engineer, Generative AI to build and scale generative models that enable robots to understand, simulate, and interact with the physical world. Responsibilities : • ...

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Helix AI Engineer, Generative AI

San Jose, CA · On-site

$200K - $400K/yr

We are looking for a Helix AI Engineer, Generative AI to build and scale generative models that enable robots to understand, simulate, and interact with the physical world. This role focuses on ...

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Helix AI Engineer, Generative AI

San Jose, CA · On-site

$200K - $400K/yr

We are looking for a Helix AI Engineer, Generative AI to build and scale generative models that enable robots to understand, simulate, and interact with the physical world. This role focuses on ...

Writer

Software engineer, generative AI

San Francisco, CA · On-site

$111K - $304K/yr

As a software engineer, generative ai at WRITER, you'll be at the forefront of expanding human capacity by building the secure, scalable foundation that allows our generative AI solutions to thrive ...

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All JobsGenerative Ai Engineer JobsGenerative Ai Engineer Jobs in California

Generative Ai Engineer information

See California salary details

$37.5K

$114.3K

$189K

How much do generative ai engineer jobs pay per year?

As of Sep 5, 2026, the average yearly pay for generative ai engineer in California is $114,347.00, according to ZipRecruiter salary data. Most workers in this role earn between $81,900.00 and $149,500.00 per year, depending on experience, location, and employer.

What is a generative AI engineer?

A Generative AI Engineer is a specialized software engineer who designs, develops, and optimizes AI models that generate content such as text, images, audio, or video. They work with deep learning frameworks, train large-scale models, and fine-tune pre-trained architectures to improve performance. Their role involves data preprocessing, model deployment, and continuous optimization to enhance AI-generated outputs. Generative AI Engineers typically collaborate with data scientists, researchers, and product teams to integrate AI solutions into applications and services.

What does a generative AI engineer do?

As a Generative AI Engineer, your typical responsibilities involve designing, developing, and optimizing generative models for tasks such as image synthesis, natural language generation, or data augmentation. You will often collaborate closely with data scientists, researchers, and product teams to translate business or research goals into scalable AI solutions. Day-to-day work may include experimenting with different neural network architectures, optimizing model performance, and deploying models to production environments. Many roles also offer opportunities to contribute to publications or open-source projects, and there is strong potential for career growth into lead engineering or research positions as you gain experience.

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

To thrive as a Generative AI Engineer, you need a deep understanding of machine learning, deep learning architectures (such as GANs and transformers), and proficiency in programming languages like Python, along with a degree in computer science, engineering, or a related field. Familiarity with frameworks such as TensorFlow, PyTorch, and relevant cloud platforms, as well as certifications in AI or ML, are highly valuable. Strong problem-solving skills, creativity, and effective communication help engineers collaborate and innovate within diverse, multidisciplinary teams. These skills are critical for developing advanced AI models, driving continuous improvement, and successfully translating complex research into practical applications.

How do I become a generative AI engineer?

To become a generative AI engineer, you should have a strong foundation in programming languages such as Python, experience with machine learning frameworks like TensorFlow or PyTorch, and knowledge of deep learning models such as GANs or transformers. Gaining expertise through relevant coursework, online tutorials, and hands-on projects is essential, along with understanding data preprocessing and model evaluation. Building a portfolio of AI projects and staying updated with the latest research can also improve job prospects in this field.

What is the salary of a generative AI engineer?

The salary of a generative AI engineer typically ranges from $100,000 to $180,000 annually, depending on experience, location, and company size. Senior roles or those with specialized skills in deep learning and machine learning frameworks may earn higher compensation, often including bonuses and stock options.

What are the most commonly searched types of Generative Ai Engineer jobs in California?

The most popular types of Generative Ai Engineer jobs in California are:

What are popular job titles related to Generative Ai Engineer jobs in California?

For Generative Ai Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching Generative Ai Engineer jobs in California look for?

The top searched job categories for Generative Ai Engineer jobs in California are:

What cities in California are hiring for Generative Ai Engineer jobs?

Cities in California with the most Generative Ai Engineer job openings:

Generative Ai Engineer jobs near you
Infographic showing various Generative Ai Engineer job openings in California as of August 2026, with employment types broken down into 77% Full Time, 20% Part Time, and 3% Contract. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $114,347 per year, or $55 per hour.

Artificial Intelligence (Generative AI) Engineer

Beehive AI, Inc.

San Francisco, CA • On-site

$150 - $230/hr

Other

Posted 4 days ago

Job description

Artificial Intelligence (Generative AI) Engineer

San Francisco Bay Area (on-site) or USA (remote)

Job Type

Full Time

About the Company

Beehive AI provides a generative AI platform designed specifically for an organization’s unique qualitative data. With self-learning language models, validated by human experts, and built-in statistical analysis, research and insights leaders can quickly, accurately, and safely analyze their qualitative data, and combine it with quantitative data, to generate more robust customer insights.
Beehive AI ingests data from any insights program running in the organization, breaking down silos between data sets and creating a more consistent approach to analysis. Unlike traditional ML/NLP tools that require manual setup and maintenance or new generative AI innovation that rely on generic LLMs and can put your corporate data at risk , Beehive AI uses generative AI and LLMs that are designed specifically for your organization, so you can safely and easily analyze qualitative data at scale, combine it with your quantitative data, and generate robust insights that more accurately reflect your business and your customer at any given point in time.

About the Position

As an Artificial Intelligence (Generative AI) Engineer, you will work on designing, developing, experimenting and maintaining various components of the Beehive AI algorithms and LLMs. You will need to work, often independently, on the full cycle of development starting from researching and experimenting with generative AI solutions all the way to implementing and productionalizing it.

Requirements

Advanced Degree : PhD in Computer Science, AI, Linguistics, Applied Physics or related fields, with a focus on AI and natural language processing.

Experience with LLMs and PyTorch : Extensive experience with large language models and proficiency in PyTorch.

Analytical and Problem-Solving Skills : Ability to address complex challenges in model training and optimization.

Communication and Collaboration Skills : Effective communication skills for conveying technical concepts and collaborating with cross-functional teams.

Innovation and Continuous Learning : Passion for staying updated with the latest trends in AI and machine learning.

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