Mlops Engineer Jobs in California
Mlops Engineer jobs in California are among the most active in the country, concentrated in artificial intelligence infrastructure, cloud platform engineering, and large-scale model deployment across technology, financial services, and biotechnology. The largest hiring clusters are in the San Francisco Bay Area, Los Angeles, and San Diego, where companies like Google, Meta, and Nvidia maintain deep mlops engineering teams. The most in-demand specialties are ML pipeline automation, model monitoring and observability, and Kubernetes-based deployment infrastructure. Find a role that fits below and apply directly.
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Showing 5 of 47+ Mlops Engineer jobs
About The Team
Grindr is an AI-native platform powering how millions of gay people connect globally. With 15M+ monthly users, 130B+ annual messages, and a team of fewer than 200, we move fast, stay lean, and tackle technical problems at a scale few companies ever see. We at Grindr believe that AI can revolutionize the dating industry. As a Staff MLOps Engineer, you will build and own the infrastructure, tooling, and scalable systems that make high-impact AI possible. You’ll architect and maintain the platforms that power data ingestion, feature computation, model training, automated evaluation, deployment, and ongoing monitoring for the ML teams building recommendations, LLM-based experiences, ads, visual search, growth, and trust & safety. You will design foundational systems that allow our ML engineers to experiment faster, ship models more reliably, and operate them with confidence in production. At Grindr, we operate in Grindr Mode. Moderately hardcore day to day, truly hardcore when it counts. It’s about doing great work without burning out. Outcomes over outputs. Smart, driven people who raise your bar, with room to live full lives. This is a hybrid role based in our Bay Area (SF or Palo Alto) or our Chicago offices and will require you to be in office Tuesdays and Thursdays.
About The Job
- Build and maintain end-to-end ML pipelines for data ingestion, feature computation, model training, validation, deployment, and inference, all at substantial scale of data
- Stand up and manage a feature store, ensuring feature consistency, lineage, and reuse across teams
- Expertise with best in class tools for managing deployment, scheduling, and environments and how to use them in the specialized regime of ML Infrastructure
- Develop automated model deployment workflows with CI/CD, safe rollout strategies, and reproducibility guarantees
- Implement monitoring and observability for ML systems, including data quality checks, drift detection, performance metrics, and alerting
- Build and support training environments with experiment tracking, distributed training, hyperparameter tuning, and artifact and environment management
- Collaborate with ML engineers and data engineers to streamline workflows, improve model iteration speed, and enforce MLOps best practices
- Ensure reliability, scalability, and maintainability of ML systems through strong engineering and operational rigor
Role Requirements
- Bachelor’s degree in CS, Engineering, Mathematics, or related field
- 5+ years experience in MLOps, ML platform engineering, ML infrastructure, or similar roles
- Strong experience building production ML pipelines and supporting end-to-end ML workflows
- Excellent engineering fundamentals: Python, SQL, bash, Git
- Experience with big data and distributed compute: Snowflake, Spark/pySpark, Airflow, Kubernetes, Docker, Helm
- Experience with ML frameworks (PyTorch, TensorFlow) sufficient to support training pipelines and deployment workflows
- Strong understanding of cloud platforms (AWS, GCP, or Azure)
- Ability to produce well-engineered, maintainable software with tests, documentation, and operational rigor
- Experience with data quality frameworks, observability tooling, or experiment tracking systems
You May Thrive In This Role If You
- Experience implementing full model lifecycle management (from data → training → deployment → monitoring)
- Experience with vector databases, embeddings pipelines, or retrieval systems
- Familiarity with NLP/LLM-based data pipelines or image/vision data workflows
- Experience with recommendation system infrastructure
- Strong grasp of classical ML concepts as they relate to platform design
- Knowledge of data governance, compliance, retention, and classification
- Track record of partnering with research/ML teams to operationalize models at scale
Benefits And Perks
- Health, Dental & Vision Full premium coverage for you. Partial coverage for dependents
- Family Formation Up to $300,000 in fertility and family-building support, covering IVF, surrogacy, egg freezing, and adoption
- Retirement: 401(k) with 6% match and immediate vesting
- Compensation: Industry-competitive compensation, company bonus, and equity for every employee
- Gender-Affirming Care: Industry-leading gender-affirming offerings with up to 90% cost coverage, access to Included Health, monthly stipends for HRT, and more
- Time Off & Rest Flexible vacation policy. Two company-wide rest weeks per year
- Other Benefits: Monthly stipends for cell phone, internet, wellness, food, and commuting, breakfast/lunch
About Grindr
Grindr is building the Global Gayborhood in your Pocket. With more than 15 million monthly active users, Grindr has become a fundamental part of the gay community and is charting a path to make the world more free, equal, and just. As a public company, we're moving into the next chapter: becoming an AI-native organization that's redefining what's possible for our community. We're embedding AI into our product and operations, moving with startup speed while operating at scale. Everything we do is rooted in impact for our users and outcomes for the business. This isn't social work, it’s impact through excellence. Business success is what gives us the power to serve our community and the seat at tables others can't reach. How we work is defined by Grindr Mode, our operating model that's moderately hardcore day-to-day and truly hardcore when it counts. We think like owners, drive real outcomes, create clarity through action, and push each other to go further than we thought possible. We’re headquartered in West Hollywood with offices in the Bay Area, Chicago, and New York. Our work extends beyond the product through Grindr for Equality, our nonprofit that's partnered with 100+ community organizations globally to advance safety, health, and human rights for LGBTQ+ people worldwide. We're profitable, growing, and building a team of smart, driven people who bring different backgrounds, experiences, and perspectives to the work. Want to build technology at a massive scale that serves millions? Come join us!
Grindr is an equal-opportunity employer.
To learn more about how we handle the personal data of applicants, visit our Employee and Candidate Privacy Policy.
About The Team
Grindr is an AI-native platform powering how millions of gay people connect globally. With 15M+ monthly users, 130B+ annual messages, and a team of fewer than 200, we move fast, stay lean, and tackle technical problems at a scale few companies ever see. We at Grindr believe that AI can revolutionize the dating industry. As a Staff MLOps Engineer, you will build and own the infrastructure, tooling, and scalable systems that make high-impact AI possible. You’ll architect and maintain the platforms that power data ingestion, feature computation, model training, automated evaluation, deployment, and ongoing monitoring for the ML teams building recommendations, LLM-based experiences, ads, visual search, growth, and trust & safety. You will design foundational systems that allow our ML engineers to experiment faster, ship models more reliably, and operate them with confidence in production. At Grindr, we operate in Grindr Mode. Moderately hardcore day to day, truly hardcore when it counts. It’s about doing great work without burning out. Outcomes over outputs. Smart, driven people who raise your bar, with room to live full lives. This is a hybrid role based in our Bay Area (SF or Palo Alto) or our Chicago offices and will require you to be in office Tuesdays and Thursdays.
About The Job
- Build and maintain end-to-end ML pipelines for data ingestion, feature computation, model training, validation, deployment, and inference, all at substantial scale of data
- Stand up and manage a feature store, ensuring feature consistency, lineage, and reuse across teams
- Expertise with best in class tools for managing deployment, scheduling, and environments and how to use them in the specialized regime of ML Infrastructure
- Develop automated model deployment workflows with CI/CD, safe rollout strategies, and reproducibility guarantees
- Implement monitoring and observability for ML systems, including data quality checks, drift detection, performance metrics, and alerting
- Build and support training environments with experiment tracking, distributed training, hyperparameter tuning, and artifact and environment management
- Collaborate with ML engineers and data engineers to streamline workflows, improve model iteration speed, and enforce MLOps best practices
- Ensure reliability, scalability, and maintainability of ML systems through strong engineering and operational rigor
Role Requirements
- Bachelor’s degree in CS, Engineering, Mathematics, or related field
- 5+ years experience in MLOps, ML platform engineering, ML infrastructure, or similar roles
- Strong experience building production ML pipelines and supporting end-to-end ML workflows
- Excellent engineering fundamentals: Python, SQL, bash, Git
- Experience with big data and distributed compute: Snowflake, Spark/pySpark, Airflow, Kubernetes, Docker, Helm
- Experience with ML frameworks (PyTorch, TensorFlow) sufficient to support training pipelines and deployment workflows
- Strong understanding of cloud platforms (AWS, GCP, or Azure)
- Ability to produce well-engineered, maintainable software with tests, documentation, and operational rigor
- Experience with data quality frameworks, observability tooling, or experiment tracking systems
You May Thrive In This Role If You
- Experience implementing full model lifecycle management (from data → training → deployment → monitoring)
- Experience with vector databases, embeddings pipelines, or retrieval systems
- Familiarity with NLP/LLM-based data pipelines or image/vision data workflows
- Experience with recommendation system infrastructure
- Strong grasp of classical ML concepts as they relate to platform design
- Knowledge of data governance, compliance, retention, and classification
- Track record of partnering with research/ML teams to operationalize models at scale
Benefits And Perks
- Health, Dental & Vision Full premium coverage for you. Partial coverage for dependents
- Family Formation Up to $300,000 in fertility and family-building support, covering IVF, surrogacy, egg freezing, and adoption
- Retirement: 401(k) with 6% match and immediate vesting
- Compensation: Industry-competitive compensation, company bonus, and equity for every employee
- Gender-Affirming Care: Industry-leading gender-affirming offerings with up to 90% cost coverage, access to Included Health, monthly stipends for HRT, and more
- Time Off & Rest Flexible vacation policy. Two company-wide rest weeks per year
- Other Benefits: Monthly stipends for cell phone, internet, wellness, food, and commuting, breakfast/lunch
About Grindr
Grindr is building the Global Gayborhood in your Pocket. With more than 15 million monthly active users, Grindr has become a fundamental part of the gay community and is charting a path to make the world more free, equal, and just. As a public company, we're moving into the next chapter: becoming an AI-native organization that's redefining what's possible for our community. We're embedding AI into our product and operations, moving with startup speed while operating at scale. Everything we do is rooted in impact for our users and outcomes for the business. This isn't social work, it’s impact through excellence. Business success is what gives us the power to serve our community and the seat at tables others can't reach. How we work is defined by Grindr Mode, our operating model that's moderately hardcore day-to-day and truly hardcore when it counts. We think like owners, drive real outcomes, create clarity through action, and push each other to go further than we thought possible. We’re headquartered in West Hollywood with offices in the Bay Area, Chicago, and New York. Our work extends beyond the product through Grindr for Equality, our nonprofit that's partnered with 100+ community organizations globally to advance safety, health, and human rights for LGBTQ+ people worldwide. We're profitable, growing, and building a team of smart, driven people who bring different backgrounds, experiences, and perspectives to the work. Want to build technology at a massive scale that serves millions? Come join us!
Grindr is an equal-opportunity employer.
To learn more about how we handle the personal data of applicants, visit our Employee and Candidate Privacy Policy.
See All 47 Mlops Engineer Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
Find Mlops Engineer JobsMlops Engineer Jobs by City in California
Where California roles are concentrated, by current openings.
Mlops Engineer Job Market in California
A snapshot from current California openings, updated as new roles post.
Who's Hiring
- Alvarez & Marsal4
- Adobe3
- NVIDIA3
- Discord2
- Qualcomm2
Top Industries Hiring
- Technology & Software26
- Electronics & Hardware9
- Consulting & Professional Services7
- Retail4
- Banking & Financial Services2
What California Employers Look For
The qualifications that appear most often in mlops engineer jobs across California.
- Bachelor's or master's degree in computer science, data science, or a related engineering field
- Hands-on experience with ML pipeline orchestration tools such as Kubeflow, MLflow, or Apache Airflow
- Proficiency deploying and monitoring models on cloud platforms including GCP, AWS, or Azure
- Strong programming skills in Python with experience in containerization using Docker and Kubernetes
- Familiarity with CI/CD practices applied to machine learning model training and deployment workflows
- Experience with data versioning, feature stores, and model registry tools in production environments
Mlops Engineer Jobs in California: Frequently Asked Questions
How do you become a mlops engineer in California?
There is no state-issued license required to work as a mlops engineer in California. Most California employers expect a bachelor's or master's degree in computer science, data engineering, or a related field, combined with demonstrated experience building and operating ML pipelines in production. Strong candidates pair their degree with cloud certifications from Google, AWS, or Microsoft and a portfolio of deployed model projects. California's dense network of community colleges and UC and CSU campuses also offers applied data engineering programs that feed directly into mlops roles.
Which companies hire mlops engineers in California?
Employers hiring mlops engineers in California right now include Alvarez & Marsal, Adobe, and NVIDIA, based on current listings on Migrate Mate as of June 2026. California's concentration of large technology companies, AI-focused startups, and pharmaceutical firms with computational research arms means consistent mlops hiring across multiple industries and seniority levels.
Which California cities have the most mlops engineer jobs?
The cities with the most mlops engineer openings in California are San Francisco, San Jose, and Irvine. The Bay Area dominates because of its density of AI-native companies and major tech headquarters, while Los Angeles draws mlops engineers into entertainment technology, adtech, and fintech, and San Diego's openings are anchored by its biotech corridor and defense technology contractors.
Are there remote mlops engineer jobs in California?
Yes, and more than most fields. About 23% of mlops engineer openings tied to California are remote or hybrid as of June 2026, reflecting how much of the work involves cloud infrastructure and code rather than physical hardware. Model monitoring, pipeline development, and experiment tracking are the tasks most commonly performed fully remotely, while on-site expectations tend to apply mainly to roles that involve managing GPU clusters or sensitive on-premises data environments.
How can I get hired as a mlops engineer in California with little or no experience?
The most realistic entry path is moving into mlops from an adjacent role such as data engineer, software engineer, or data scientist, where you have already worked with production systems. Large California employers including Google, Salesforce, and Apple run new-graduate and associate engineering programs that place candidates without direct mlops titles into platform or infrastructure teams where mlops skills develop on the job. Building a public portfolio of end-to-end ML pipeline projects on GitHub and earning a cloud certification from Google Cloud or AWS strengthens applications significantly for California entry-level roles.
Where can I find and apply to mlops engineer jobs in California?
You can find and apply to mlops engineer jobs in California on Migrate Mate, which lists current California openings across the Bay Area, Los Angeles, San Diego, and beyond. Search the listings, find roles that match your background and target location, and apply directly to the ones that fit.
See All 47 Mlops Engineer Jobs in California
Find roles in California that match your experience and apply in just a few clicks.
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