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Machine Learning Data Engineer Jobs in California

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

We're seeking an outstanding ML Engineer to join our data team and help build out best-in-class machine learning solutions on our platform, powering innovative solutions in marketing & sales and ...

Pattern AI

Machine Learning Engineer

San Mateo, CA

We're seeking an outstanding ML Engineer to join our data team and help build out best-in-class machine learning solutions on our platform, powering innovative solutions in marketing & sales and ...

Skyworks

Sr. Data Scientist

Irvine, CA

$114K - $220K/yr

Requisition ID: 77724 Description Skyworks Solutions is seeking a Machine Learning and Data Engineer to join our rapidly growing AI/ML team in Irvine, CA. This is a highly technical, hands-on role ...

Apple

Machine Learning Engineer

Cupertino, CA · On-site

Bachelor's Degree in Computer Science, Statistics, Data Mining, Machine Learning, Operations Research, or related field. Proficiency in one or more object-oriented programming languages such as ...

Hirekeyz Inc

Senior Data Engineer

Sunnyvale, CA · On-site

$124K - $169K/yr

Senior Data Engineer Location: Sunnyvale, CA Job Type: Contract/W2 Key Skills: Tableau, Python ... A solid understanding and experience with Machine learning is a valuable addition. Key ...

Cypress HCM

Data Engineer

Mountain View, CA

$70 - $95/hr

Data Engineer The Cloud services group is looking for world class engineers to join our technology ... Strong skills in algorithms, Data structures, UNIX/Linux, Scripting, Machine Learning (especially ...

Donato Technologies, Inc

Senior Data Engineer

Sunnyvale, CA · On-site

$124K - $169K/yr

Senior Data Engineer Location: Sunnyvale, CA (Local candidates preferred) or with in the time zone ... A solid understanding and experience with Machine learning is a valuable addition. Key ...

Winaxis

Machine Learning Engineer

Fremont, CA · On-site

Machine Learning Engineer Location: Fremont, CA (Local) Onsite interview Duration: 12+ Mos H1B Only ... Build and maintain end-to-end machine learning pipelines, from data collection and preprocessing to ...

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All JobsMachine Learning Data Engineer JobsMachine Learning Data Engineer Jobs in California

Machine Learning Data Engineer information

See California salary details

$43.9K

$128K

$175.2K

How much do machine learning data engineer jobs pay per year?

As of Jul 18, 2026, the average yearly pay for machine learning data engineer in California is $128,018.00, according to ZipRecruiter salary data. Most workers in this role earn between $113,000.00 and $135,700.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Machine Learning Data Engineer position, and why are they important?

To thrive as a Machine Learning Data Engineer, you typically need strong programming skills in Python or Scala, a deep understanding of data structures, algorithms, and machine learning concepts, as well as a degree in computer science or a related field. Experience with big data tools like Spark, Hadoop, and cloud platforms such as AWS or Azure, along with knowledge of data pipelines and ETL processes, is highly valuable; certifications in these areas can be advantageous. Problem-solving ability, attention to detail, and strong communication skills help professionals excel when working with diverse technical teams and stakeholders. These skills ensure data engineers can effectively build reliable, scalable data systems that support the development and deployment of machine learning models.

Can a data engineer become a machine learning engineer?

A data engineer can transition to a machine learning engineer role by gaining knowledge of machine learning algorithms, model development, and deployment techniques. Skills in programming languages like Python, experience with frameworks such as TensorFlow or PyTorch, and understanding of data pipelines are essential for this progression.

What is a Machine Learning Data Engineer job?

A Machine Learning Data Engineer is responsible for designing, building, and maintaining the data infrastructure that supports machine learning models. They develop data pipelines, ensure data quality, and optimize data storage for efficient processing. This role involves working with large-scale datasets, implementing ETL processes, and collaborating with data scientists to deploy machine learning models. Strong knowledge of databases, cloud platforms, and programming languages like Python and SQL is essential. Their work enables organizations to leverage machine learning effectively by providing reliable and scalable data solutions.

Will MLE be replaced by AI?

Machine Learning Data Engineers (MLEs) design, build, and maintain data pipelines and models that AI systems rely on. While AI automation tools can handle some tasks, MLE skills in data engineering, programming, and system architecture remain essential for developing and managing AI infrastructure effectively. The role is expected to evolve with advancements in AI, but it is unlikely to be fully replaced in the near future.

What are the typical daily responsibilities of a Machine Learning Data Engineer?

As a Machine Learning Data Engineer, your daily responsibilities often include designing, building, and maintaining data pipelines that efficiently move and transform data for machine learning applications. You may clean, preprocess, and validate large datasets, optimize storage solutions, and work closely with data scientists to ensure data is accessible and usable for model training and evaluation. Regular collaboration with software engineers and business analysts is common to align project goals and solve data-related challenges. Staying up to date with the latest tools and technologies is also important, as you'll help enable scalable and efficient deployment of machine learning solutions.

What engineers make $500,000?

Senior machine learning data engineers with extensive experience, advanced skills in data architecture, and proficiency in tools like Spark and cloud platforms can earn $500,000 or more annually, especially in high-cost-of-living areas or within large tech companies. Achieving this level often requires a combination of technical expertise, leadership responsibilities, and industry reputation.

What is a $900000 AI job?

A $900,000 AI-related job typically refers to high-level roles in artificial intelligence, such as senior machine learning engineers or AI research directors, often requiring advanced skills in deep learning, data modeling, and experience with tools like TensorFlow or PyTorch. These positions usually involve leadership responsibilities, strategic planning, and may require multiple years of specialized experience or advanced degrees. Compensation at this level reflects the value of expertise in developing and deploying complex AI systems in competitive industries.
What are popular job titles related to Machine Learning Data Engineer jobs in California? For Machine Learning Data Engineer jobs in California, the most frequently searched job titles are:
What job categories do people searching Machine Learning Data Engineer jobs in California look for? The top searched job categories for Machine Learning Data Engineer jobs in California are:
Machine Learning Data Engineer jobs near you
Infographic showing various Machine Learning Data Engineer job openings in California as of July 2026, with employment types broken down into 1% As Needed, 83% Full Time, 12% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $128,018 per year, or $61.5 per hour.
Machine Learning Engineer

Machine Learning Engineer

Swish Analytics

San Francisco, CA • Remote

$160K/yr

Full-time

Posted 11 hours ago

Job description

Swish Analytics is a sports analytics, betting and fantasy startup building the next generation of predictive sports analytics data products. We believe that oddsmaking is a challenge rooted in engineering, mathematics, and sports betting expertise; not intuition. We're looking for team-oriented individuals with an authentic passion for accurate and predictive real-time data who can execute in a fast-paced, creative, and continually-evolving environment without sacrificing technical excellence. Our challenges are unique, so we hope you are comfortable in uncharted territory and passionate about building systems to support products across a variety of industries and enterprise clients.

The Data Science team is hiring an experienced Machine Learning Engineer with a background building machine learning and statistical modeling frameworks from scratch. They can assist with optimizing the different aspects of the modeling process (Data Validation, Data Visualization, Data Stores & Structures, Feature Engineering, Model Training & Evaluation, Deployments) and improving a variety of Swish products. They will know when to “roll your own” and when to outsource a particular step in the modeling process. They will engineer custom solutions to solve complex data-related sports challenges across multiple leagues.

This position is 100% remote

Responsibilities:

  • Design, prototype, implement, evaluate, optimize systems to generate sports datasets and predictions with high accuracy and low latency.

  • Evaluate internal modeling frameworks and tools to optimize data scientist's modeling workflow.

  • Build, test, deploy and maintain production systems.

  • Work closely with DevOps and Data Engineering teams to assist with implementation, optimization and scale workloads on Kubernetes using CI/CD, automation tools and scripting languages.

  • Support maintenance and optimization of cloud-native EDW and ETL solutions.

  • Maintain and promote best practices for software development, including deployment process, documentation, and coding standards.

  • Experience applying large scale data processing techniques to develop scalable and innovative sports betting products.

  • Use extensive experience to build, test, debug, and deploy production-grade components.

  • Experience applying large scale data processing techniques to develop scalable and innovative sports betting products.

  • Participate in development of database structures that fit into the overall architecture of Swish systems

Qualifications:

  • Masters degree in Computer Science, Applied Mathematics, Data Science, Computational Physics/Chemistry or related technical subject area

  • 5+ years of demonstrated experience developing and delivering clean and efficient production code to serve business needs

  • A proven background in quantitative analytics, trading, or engineering is required for this position

  • Demonstrated experience developing data science modeling systems and infrastructure at scale

  • Experience with Python and exposure to modern machine learning frameworks

  • Proficient in SQL; experience with MySQL

  • Background and/or interest in Rust preferred

  • Affinity for teamwork and collaboration with others to solve problems, share knowledge, and provide feedback

  • Strong communication skills when discussing technical concepts with technical and non-technical colleagues

Base salary: starting at $160,000 base plus bonus potential

Swish Analytics is an Equal Opportunity Employer. All candidates who meet the qualifications will be considered without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, pregnancy status, genetic, military, veteran status, marital status, or any other characteristic protected by law. The position responsibilities are not limited to the responsibilities outlined above and are subject to change. At the employer’s discretion, this position may require successful completion of background and reference checks.