AI ML Engineer Visa Sponsorship Jobs in California
California is the largest market in the U.S. for AI/ML engineer visa sponsorship, driven by tech giants like Google, Meta, Apple, and Amazon across the Bay Area, plus a growing cluster of AI-focused startups in San Francisco and Los Angeles. FAANG-scale employers and well-funded labs here sponsor H-1B visa and O-1 visas at high volume.
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Get Access To All JobsINTRODUCTION
The Quality ML/AI Engineer is a highly technical and analytical role supporting the Quality organization and broader enterprise initiatives through the design, development, and deployment of advanced data, machine learning, and artificial intelligence solutions. This role focuses on building scalable and compliant analytical frameworks that connect Quality and business systems to enable proactive product monitoring, advanced quality metrics, improved decision-making, and operational efficiencies.
This position is responsible for developing and maintaining code-based analytical solutions, integrating data across GxP and non-GxP systems, and applying statistical, machine learning, and AI techniques to regulated data sets to support compliance, post-market surveillance, product performance monitoring, and continuous improvement initiatives. The role partners closely with Quality, Global Technology Services, Manufacturing/Operations, R&D, and cross-functional business stakeholders to ensure solutions are technically robust, scalable, and aligned with regulatory expectations.
ROLE AND RESPONSIBILITIES
Data Engineering, Architecture & Integration
- Design, develop, and maintain scalable data pipelines and integrations connecting Quality, Manufacturing, Post-Market, and enterprise systems such as QMS, ERP, PLM, CRM, complaint handling, and adverse event platforms.
- Develop and maintain production-ready analytical code using tools such as Python, SQL, R, or similar programming languages to support ETL/ELT processes and advanced analytics.
- Build scalable data models, repositories, and analytical frameworks that support cross-platform insights while maintaining data integrity, traceability, and compliance in regulated environments.
- Ensure data solutions align with data governance standards, computerized system compliance requirements, and validated system expectations.
Machine Learning & Artificial Intelligence Development
- Develop, validate, and maintain machine learning and AI models to support quality monitoring, operational efficiency, predictive analytics, and business insights.
- Apply statistical, machine learning, and AI methodologies based on business needs, data maturity, risk considerations, and intended use.
- Partner with Quality, Operations, and Regulatory stakeholders to define model intent, limitations, and appropriate application within Quality Management Systems and business operations.
- Build explainable, transparent, and scalable models suitable for regulated and non-regulated decision-support environments.
- Contribute to enterprise-wide ML/AI initiatives that drive innovation, automation, and continuous improvement across the organization.
Quality Metrics, Monitoring & Reporting
- Design and automate dashboards, reporting tools, and monitoring solutions using code-based and business intelligence platforms to provide near real-time visibility into product and system performance.
- Translate complex analytical outputs into clear, actionable insights for leadership, Management Review activities, and cross-functional teams.
- Continuously assess data quality, model effectiveness, and reporting relevance, refining analytical approaches as business and regulatory needs evolve.
Compliance, Validation & Risk-Based Assurance
- Ensure analytical tools, ML models, and AI solutions are developed and maintained in compliance with applicable Medical Device, Pharmaceutical, and Combination Product regulations.
- Support risk-based validation, change management, and documentation activities for analytical systems in accordance with internal procedures and regulatory expectations.
- Maintain documentation related to model design, assumptions, data sources, limitations, and ongoing performance monitoring appropriate for GxP environments.
Collaboration & Continuous Improvement
- Collaborate cross-functionally to translate business challenges into data-driven and AI-enabled solutions.
- Support investigations, audits, inspections, and regulatory interactions related to data integrity, quality metrics, and analytical systems.
- Identify opportunities to improve efficiency, increase automation, and reduce manual reporting efforts across the Quality organization and broader enterprise.
- Contribute to a collaborative, innovative, and continuous improvement-focused culture.
BASIC QUALIFICATIONS
- Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Life Sciences, Chemistry, or a related technical field.
- 1–2 years of experience in data science, analytics, machine learning, software-enabled analytics, or related technical roles, preferably within a regulated GxP environment.
- Hands-on experience developing and maintaining analytical or production code using tools such as Python, SQL, R, or similar programming languages.
- Experience building reports, analyzing data, and solving business problems through data-driven insights and automation.
- Exposure to machine learning and/or AI model development for business, operational, or quality-related applications.
- Experience integrating data across multiple enterprise systems and structured or unstructured data sources.
- Understanding of Medical Device, Pharmaceutical, and/or Combination Product quality systems, regulatory expectations, and computerized system assurance concepts.
- Knowledge of data integrity principles, validation approaches, and risk-based methodologies in regulated environments.
- Strong analytical, problem-solving, communication, documentation, and stakeholder collaboration skills, with the ability to work effectively in fast-paced cross-functional environments.
- Experience working with validated quality software, along with proficiency in Microsoft Office applications (Excel, Word, PowerPoint, Visio), and a self-motivated, detail-oriented approach with flexibility to support evolving business needs.
Glaukos Corporation is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex including sexual orientation and gender identity, national origin, disability, protected Veteran Status, or any other characteristic protected by applicable federal, state, or local law.
All offers of employment are contingent upon the successful completion of a background check, including successfully passing a drug screen, based on the position and local regulations.
INTRODUCTION
The Quality ML/AI Engineer is a highly technical and analytical role supporting the Quality organization and broader enterprise initiatives through the design, development, and deployment of advanced data, machine learning, and artificial intelligence solutions. This role focuses on building scalable and compliant analytical frameworks that connect Quality and business systems to enable proactive product monitoring, advanced quality metrics, improved decision-making, and operational efficiencies.
This position is responsible for developing and maintaining code-based analytical solutions, integrating data across GxP and non-GxP systems, and applying statistical, machine learning, and AI techniques to regulated data sets to support compliance, post-market surveillance, product performance monitoring, and continuous improvement initiatives. The role partners closely with Quality, Global Technology Services, Manufacturing/Operations, R&D, and cross-functional business stakeholders to ensure solutions are technically robust, scalable, and aligned with regulatory expectations.
ROLE AND RESPONSIBILITIES
Data Engineering, Architecture & Integration
- Design, develop, and maintain scalable data pipelines and integrations connecting Quality, Manufacturing, Post-Market, and enterprise systems such as QMS, ERP, PLM, CRM, complaint handling, and adverse event platforms.
- Develop and maintain production-ready analytical code using tools such as Python, SQL, R, or similar programming languages to support ETL/ELT processes and advanced analytics.
- Build scalable data models, repositories, and analytical frameworks that support cross-platform insights while maintaining data integrity, traceability, and compliance in regulated environments.
- Ensure data solutions align with data governance standards, computerized system compliance requirements, and validated system expectations.
Machine Learning & Artificial Intelligence Development
- Develop, validate, and maintain machine learning and AI models to support quality monitoring, operational efficiency, predictive analytics, and business insights.
- Apply statistical, machine learning, and AI methodologies based on business needs, data maturity, risk considerations, and intended use.
- Partner with Quality, Operations, and Regulatory stakeholders to define model intent, limitations, and appropriate application within Quality Management Systems and business operations.
- Build explainable, transparent, and scalable models suitable for regulated and non-regulated decision-support environments.
- Contribute to enterprise-wide ML/AI initiatives that drive innovation, automation, and continuous improvement across the organization.
Quality Metrics, Monitoring & Reporting
- Design and automate dashboards, reporting tools, and monitoring solutions using code-based and business intelligence platforms to provide near real-time visibility into product and system performance.
- Translate complex analytical outputs into clear, actionable insights for leadership, Management Review activities, and cross-functional teams.
- Continuously assess data quality, model effectiveness, and reporting relevance, refining analytical approaches as business and regulatory needs evolve.
Compliance, Validation & Risk-Based Assurance
- Ensure analytical tools, ML models, and AI solutions are developed and maintained in compliance with applicable Medical Device, Pharmaceutical, and Combination Product regulations.
- Support risk-based validation, change management, and documentation activities for analytical systems in accordance with internal procedures and regulatory expectations.
- Maintain documentation related to model design, assumptions, data sources, limitations, and ongoing performance monitoring appropriate for GxP environments.
Collaboration & Continuous Improvement
- Collaborate cross-functionally to translate business challenges into data-driven and AI-enabled solutions.
- Support investigations, audits, inspections, and regulatory interactions related to data integrity, quality metrics, and analytical systems.
- Identify opportunities to improve efficiency, increase automation, and reduce manual reporting efforts across the Quality organization and broader enterprise.
- Contribute to a collaborative, innovative, and continuous improvement-focused culture.
BASIC QUALIFICATIONS
- Bachelor’s degree in Computer Science, Data Science, Artificial Intelligence, Engineering, Life Sciences, Chemistry, or a related technical field.
- 1–2 years of experience in data science, analytics, machine learning, software-enabled analytics, or related technical roles, preferably within a regulated GxP environment.
- Hands-on experience developing and maintaining analytical or production code using tools such as Python, SQL, R, or similar programming languages.
- Experience building reports, analyzing data, and solving business problems through data-driven insights and automation.
- Exposure to machine learning and/or AI model development for business, operational, or quality-related applications.
- Experience integrating data across multiple enterprise systems and structured or unstructured data sources.
- Understanding of Medical Device, Pharmaceutical, and/or Combination Product quality systems, regulatory expectations, and computerized system assurance concepts.
- Knowledge of data integrity principles, validation approaches, and risk-based methodologies in regulated environments.
- Strong analytical, problem-solving, communication, documentation, and stakeholder collaboration skills, with the ability to work effectively in fast-paced cross-functional environments.
- Experience working with validated quality software, along with proficiency in Microsoft Office applications (Excel, Word, PowerPoint, Visio), and a self-motivated, detail-oriented approach with flexibility to support evolving business needs.
Glaukos Corporation is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex including sexual orientation and gender identity, national origin, disability, protected Veteran Status, or any other characteristic protected by applicable federal, state, or local law.
All offers of employment are contingent upon the successful completion of a background check, including successfully passing a drug screen, based on the position and local regulations.
AI ML Engineer Job Roles in California
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Search AI ML Engineer Jobs in CaliforniaAI ML Engineer Jobs in California: Frequently Asked Questions
Which companies sponsor visas for AI/ML engineers in California?
The most active sponsors for AI/ML engineers in California include Google, Meta, Apple, Amazon, Microsoft, Nvidia, Salesforce, and Adobe, all with large Bay Area presences. Beyond big tech, AI-focused companies like Anthropic, OpenAI, Scale AI, and Databricks in San Francisco also have established sponsorship programs. USCIS H-1B disclosure data confirms these employers file petitions in high volume for machine learning and AI research roles specifically.
Which visa types are most common for AI/ML engineer roles in California?
The H-1B is the most common visa for AI/ML engineers in California, as these roles consistently qualify as specialty occupations requiring a bachelor's degree or higher in computer science, statistics, or a related field. The O-1A is a realistic option for engineers with published research, patents, or recognized industry contributions. Candidates already in the U.S. on F-1 OPT or STEM OPT extension also frequently transition into employer-sponsored H-1B status through California employers.
How to find ai ml engineer visa sponsorship jobs in California?
Migrate Mate filters AI/ML engineer jobs specifically by visa sponsorship availability and state, making it straightforward to identify California employers actively hiring international candidates. Because many job postings don't explicitly state sponsorship willingness, Migrate Mate's curated data removes the guesswork. Filtering by California surfaces roles across the Bay Area, Los Angeles, and San Diego from employers with a verified history of sponsoring AI and machine learning engineers.
Which cities in California have the most AI/ML engineer sponsorship jobs?
The San Francisco Bay Area, including San Francisco, Mountain View, Sunnyvale, and Menlo Park, accounts for the largest share of AI/ML sponsorship roles in California. Seattle-adjacent overflow aside, Los Angeles has grown significantly as a second hub, particularly around Santa Monica and Culver City where entertainment-tech and AI startups have concentrated. San Diego has a smaller but active cluster tied to biotech AI and defense-adjacent machine learning work.
Are there state-specific considerations for AI/ML engineers seeking sponsorship in California?
California's prevailing wage requirements under Department of Labor rules apply to all H-1B filings, and given the state's high cost of living, prevailing wage levels for AI/ML roles in metro areas like San Francisco and San Jose tend to be among the highest in the country. California also has strong university pipelines from UC Berkeley, Stanford, UCLA, and UCSD that influence how employers recruit internationally, making research and graduate credentials particularly valued in sponsorship decisions.
What is the prevailing wage for sponsored ai ml engineer jobs in California?
U.S. employers sponsoring a visa must pay at least the prevailing wage, which is what workers in the same role, area, and experience level typically earn. The Department of Labor sets this rate to make sure companies aren't hiring foreign workers simply because they'd accept lower pay than a U.S. worker. It varies by job title, location, and experience. You can look up current prevailing wage rates for any occupation and location using the OFLC Wage Search page.