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Generative Ai Developer Jobs in California (NOW HIRING)

CYNET SYSTEMS

Java AI Developer

Palo Alto, CA · On-site

$53 - $57/hr

Seeking a Java AI Developer with strong programming skills and experience in cloud services to ... Familiarity with AI/ML frameworks (TensorFlow, PyTorch) and experience with generative AI tools (e ...

Hippocratic AI

Staff AI Engineer

Menlo Park, CA · On-site

$190 - $270/hr

About the Role As a Staff AI Engineer at Hippocratic AI, you'll set the technical direction for voice‑based generative AI in healthcare. You'll architect the intelligent systems that power our ...

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All JobsGenerative Ai Developer JobsGenerative Ai Developer Jobs in California

Generative Ai Developer information

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$99

How much do generative ai developer jobs pay per hour?

As of Aug 23, 2026, the average hourly pay for generative ai developer in California is $44.70, according to ZipRecruiter salary data. Most workers in this role earn between $23.27 and $54.09 per hour, depending on experience, location, and employer.

What is a generative AI developer?

A Generative AI Developer is a technology professional who specializes in designing, building, and deploying artificial intelligence systems that can create new content, such as text, images, audio, or code. They work with advanced machine learning models, like generative adversarial networks (GANs) or large language models, to enable computers to produce original outputs. These developers often collaborate with data scientists, researchers, and product teams to integrate AI-generated content into software applications and business solutions.

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

To thrive as a Generative AI Developer, you need strong programming skills (especially in Python), a deep understanding of machine learning concepts, and an advanced degree in computer science or a related field. Familiarity with frameworks like TensorFlow, PyTorch, and experience with cloud platforms or model deployment tools are typically required. Creative problem-solving, adaptability, and effective collaboration are standout soft skills in this evolving field. These abilities are crucial to design, implement, and refine generative models that solve real-world problems and drive innovation.

What are some common challenges faced by generative AI developers when deploying models in production environments?

Generative AI Developers often encounter challenges such as ensuring model reliability, managing computational resource requirements, and addressing ethical considerations like data bias or content safety. Deploying generative models at scale requires robust monitoring to detect unexpected outputs or model drift, and collaboration with data engineers and product teams to optimize performance. Staying up-to-date with evolving frameworks and best practices is essential, as production environments demand both technical rigor and adaptability to new AI advancements.

What is the difference between Generative Ai Developer vs Machine Learning Engineer?

AspectGenerative Ai DeveloperMachine Learning Engineer
CredentialsBachelor's or higher in CS, AI, or related fields; experience with deep learning frameworksBachelor's or higher in CS, Data Science, or related fields; strong programming skills
Work EnvironmentDevelops AI models for content creation, chatbots, and creative applicationsBuilds and deploys ML models for various data-driven solutions across industries
Industry UsageTech, entertainment, marketing, and creative sectorsFinance, healthcare, tech, and e-commerce sectors

While both roles involve AI and machine learning, Generative Ai Developers focus on creating models that generate content, such as images or text, whereas Machine Learning Engineers develop broader ML solutions for diverse applications. The roles often overlap but differ mainly in their specific focus areas and use cases.

How to become a generative AI developer?

To become a generative AI developer, you should have a strong foundation in programming languages like Python, experience with machine learning frameworks such as TensorFlow or PyTorch, and knowledge of neural network architectures like transformers. Gaining expertise in natural language processing and deep learning, along with practical experience through projects or internships, is essential. Certifications in AI or data science can also enhance your qualifications.

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

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

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

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

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

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

Generative Ai Developer jobs near you
Infographic showing various Generative Ai Developer job openings in California as of August 2026, with employment types broken down into 81% Full Time, 17% Part Time, and 2% Contract. Highlights an 63% Physical, 4% Hybrid, and 33% Remote job distribution, with an average salary of $92,966 per year, or $44.7 per hour.

Generative AI Engineer

Aventine software

San Francisco, CA • On-site

Other

Posted 17 days ago

Job description

Forward Deployed Engineer (FDE) /GenAI Engineer

Locations: New Jersey / California /  North Carolina 

Role Overview

Client  is seeking a highly motivated Forward Deployed Engineer (FDE) to bridge business challenges and technology solutions through AI-assisted engineering, automation, and modern software delivery practices.

As an FDE, you will work directly with customers to translate business requirements into scalable technology solutions. You will leverage AI-native engineering approaches, Agentic SDLC practices, and automation frameworks to accelerate solution delivery while ensuring enterprise-grade quality and governance.

Key Responsibilities

  • Work closely with customers and stakeholders to understand business problems and translate them into technology solutions.
  • Design, generate, and validate AI-generated code and engineering artifacts.
  • Drive deployment of enterprise-grade applications, integrations, and AI-powered solutions.
  • Apply prompt engineering, context engineering, and AI enablement techniques.
  • Review and validate AI-generated:
  • Requirements
  • Design documents
  • Code
  • Technical specifications
  • Review and validate AI-generated:
  • Test designs
  • Test cases
  • Playwright automation scripts
  • Quality engineering assets
  • Ensure compliance with enterprise security, governance, DevSecOps, and quality standards.
  • Build and orchestrate Agentic workflows, automation pipelines, and developer productivity solutions.
  • Develop solution prototypes, proof-of-concepts, and production-ready implementations.
  • Support customer engagements, workshops, demonstrations, and solution consulting activities.

Required Skills

Technical Skills

  • Strong software engineering background with 5–15 years of experience 
  • Strong proficiency in Python, with the ability to review, validate, debug, and optimize AI-generated code for enterprise use.
  • Experience with AI-assisted development tools such as:
  • GitHub Copilot
  • Claude
  • Cursor
  • or equivalent platforms
  • Experience working with Spec-Driven Development methodologies.
  • Hands-on experience with:
  • APIs
  • REST Services
  • Microservices
  • Modern Application Architectures
  • Familiarity with:
  • CI/CD
  • DevSecOps
  • Automated Testing
  • Deployment Pipelines
  • Experience with test automation frameworks such as:
  • Playwright
  • Selenium
  • Equivalent frameworks

Professional Skills

  • Strong problem-solving and analytical capabilities.
  • Excellent customer-facing communication.
  • Consulting mindset.
  • Ability to work independently in fast-paced environments.
  • Experience collaborating with cross-functional and distributed teams.
  • Strong ownership mindset with focus on measurable business outcomes.

Preferred Qualifications

  • Banking & Financial Services (BFS) experience (Preferred, not mandatory)
  • Exposure to:
  • Agentic SDLC
  • Spec-Driven Development
  • AI-assisted delivery models
  • Understanding of:
  • Cloud-native architectures
  • Platform engineering

Apply