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Generative Ai Training Jobs in California (NOW HIRING)

Figma

AI Applied Scientist

San Francisco, CA · On-site +1

Experience working on deep learning and generative AI frameworks like PyTorch, JAX, HuggingFace etc * Experience training LLMs with Reinforcement Learning techniques such as preference-based RL (DPO ...

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Generative Ai Training information

What is generative AI training?

Generative AI training refers to the process of teaching artificial intelligence models, such as neural networks, to create new content like text, images, audio, or code. This is done by exposing the AI to large datasets so it can learn underlying patterns and generate outputs that mimic human-like creativity. The training process often involves techniques like supervised learning, unsupervised learning, or reinforcement learning, depending on the desired outcome. Generative AI is widely used in applications like chatbots, image generation, and content creation.

What are the key skills and qualifications needed to thrive in generative AI training?

To thrive in Generative AI Training, you need a strong background in machine learning, data science, and programming (especially Python), often supported by a degree in computer science or a related field. Experience with frameworks like TensorFlow, PyTorch, and familiarity with large language models and cloud platforms is typically required. Strong analytical thinking, creativity, and effective communication are essential soft skills for designing training data and refining model outputs. These skills and qualities are crucial for developing high-quality, ethical, and scalable AI systems that meet organizational goals.

What are some common challenges faced by professionals working in generative AI training roles?

Professionals in Generative AI training often encounter challenges such as ensuring data quality and diversity, combating model bias, and staying updated with fast-evolving algorithms. Collaborating closely with data scientists, engineers, and subject matter experts is essential to create robust training datasets and refine model outputs. Additionally, balancing computational resource demands with project deadlines can be demanding, making strong project management and adaptability key assets in this role.

What is the difference between Generative Ai Training vs Data Scientist?

AspectGenerative Ai TrainingData Scientist
Required CredentialsKnowledge of AI models, programming, machine learningStatistics, programming, data analysis
Work EnvironmentAI development teams, tech companies, research labsBusiness, finance, tech firms, research institutions
Industry UsageDeveloping generative models like GPT, DALL·EData analysis, predictive modeling, insights generation

Generative Ai Training focuses on developing and fine-tuning AI models that generate content, requiring expertise in AI frameworks and machine learning. Data Scientists analyze data to extract insights and build predictive models. While both roles involve programming and data skills, Generative Ai Training is specialized in AI model creation, whereas Data Scientists work broadly with data analysis across industries.

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

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

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

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

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

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

Generative Ai Training jobs near you
Infographic showing various Generative Ai Training job openings in California as of August 2026, with employment types broken down into 1% As Needed, 78% Full Time, 18% Part Time, and 3% Contract. Highlights an 86% Physical, 1% Hybrid, and 13% Remote job distribution.

Machine Learning Engineer (Generative AI & Cloud)

K-Tek Resourcing LLC

San Jose, CA • On-site

$65.25 - $87.25/hr

Other

Re-posted 8 days ago

Key responsibilities

  • Design, develop, and fine-tune Generative AI solutions using models like Google Gemini for tasks such as information extraction, document summarization, and report generation

  • Build and maintain automated MLOps pipelines for data preprocessing, model training, validation, and deployment on Google Cloud Platform

  • Partner with data scientists, software engineers, and stakeholders to define problem statements and deliver integrated AI/ML solutions

Job description


Role: Machine Learning Engineer (Generative AI & Cloud)

Work Location  : San Jose, CA (Onsite)

Duration: Long Term
Implementation Partner - HCL America

The Opportunity
  • We are seeking a talented and experienced Machine Learning Engineer. In this role, you will be at the forefront of applying Generative AI and traditional machine learning to solve complex business challenges. You will bridge the gap between data science and software engineering, taking models from concept to production and ensuring they are robust, scalable, and impactful. You''ll work with a modern tech stack centered on Python, Google Cloud Platform, and the latest in LLM technology.       
Responsibilities
  • Generative AI Development:
  • Design, develop, and fine-tune Generative AI solutions using models like Google''s Gemini for tasks such as information extraction, document summarization, and report generation.
  • Architect and implement advanced Retrieval-Augmented Generation (RAG) systems to enhance model accuracy and provide verifiable, context-aware responses.
  • Research and apply emerging GenAI techniques, such as agentic frameworks, to build more autonomous and capable systems.
  • End-to-End Machine Learning:
  • Design and deploy a wide range of ML models (classification, regression, forecasting, etc.) on Google Cloud Platform.
  • Build and maintain robust, automated MLOps pipelines for data preprocessing, feature engineering, model training, validation, and deployment using tools like Vertex AI, BigQuery. etc.
  • Conduct deep data analysis to uncover insights, validate hypotheses, and guide feature engineering for improved model performance.
  • Collaboration & Strategy:
  • Partner closely with data scientists, software engineers, and other business stakeholders to frame problem statements, define technical requirements and deliver integrated AI/ML solutions.
  • Champion best practices in software engineering and MLOps to ensure the quality, maintainability, and scalability of our machine learning systems.
  • Continuously evaluate and stay current with the latest advancements in the ML and GenAI landscape.

Required Qualifications
  • Experience: 3+ years of professional experience building and deploying machine learning models in a production environment.
  • Education: Bachelor''s degree in Computer Science, Data Science, Statistics, or a related quantitative field.
  • Programming: Advanced proficiency in Python and its core data science/ML libraries (e.g., PyTorch, scikit-learn, Pandas).
  • Data & SQL: Advanced proficiency in SQL for complex data manipulation, aggregation, and analysis.
  • Generative AI: Demonstrable, hands-on experience in prompt engineering and/or fine-tuning Large Language Models (e.g., Gemini).
  • Cloud Platform: Hands-on experience with a major cloud provider, with a strong preference for Google Cloud Platform (Google Cloud Platform).
  • MLOps: Solid understanding of MLOps principles and experience with related tools (e.g., Vertex AI, CI/CD).

Preferred Qualifications (Nice-to-Haves):
  • Master’s or PhD in a relevant field.
  • Specific experience with Google Cloud Platform services like Vertex AI, BigQuery, Google Cloud Storage, and GKE.
  • Experience building RAG systems from the ground up.
  • Proven ability to lead technical projects and mentor other engineers.

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