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Machine Learning Data Engineer Jobs in California

Working at the intersection of data science and software engineering, you translate R&D and project ... This Role As a Machine Learning Engineer, you'll work closely with our Data Scientists, Simulation ...

In 2025, we started Handshake AI and built the fastest-growing AI data business in history. We work ... engineers, data scientists, and product managers to develop and iterate on machine learning models ...

Working at the intersection of data science and software engineering, you translate R&D and project ... This Role As a Machine Learning Engineer, you'll work closely with our Data Scientists, Simulation ...

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 ...

Today, our data is used for robotics and world modeling, but the broader opportunity is advancing ... The Opportunity As a Machine Learning Engineer, you'll work on multimodal perception, VLA training ...

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 ...

Sr. Data Scientist

Irvine, CA · On-site

$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 ...

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 ...

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Showing results 1-20

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 Jun 28, 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.

Which 5 jobs will survive AI?

Machine Learning Data Engineers are likely to continue to be in demand as AI advances because they develop and maintain the data pipelines and models essential for AI systems. Roles that require complex problem-solving, creativity, and human judgment, such as healthcare professionals, educators, skilled trades, and certain managerial positions, are also expected to persist despite AI automation. These jobs often involve tasks that are difficult for AI to replicate fully.

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.

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 pipelines, cloud platforms, and machine learning frameworks can earn $500,000 or more annually, especially in high-cost-of-living areas or within large tech companies. Achieving this level typically requires a combination of technical expertise, leadership roles, and often stock options or bonuses.

Is ML a high paying job?

Machine Learning Data Engineers typically earn high salaries due to the specialized skills required, such as proficiency in programming, data modeling, and machine learning frameworks. Salaries vary by experience, location, and industry, but overall, the role is considered well-compensated within the tech field.
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 Engineer

PhysicsX

San Francisco, CA • On-site, Remote

Other

Medical, Retirement, PTO

Posted 25 days ago


Job description

Note: We are currently recruiting for multiple positions, however please only apply for the role that best aligns with your skillset and career goals.

Who We're Looking For

As a Machine Learning Engineer in Delivery, you are a problem solver who stays anchored to impact. You are someone who can grasp advanced engineering concepts across multiple industries, and excel at working directly with customers (and often side-by-side with them on-site) to embed cutting-edge AI models into tools that are useful and used.
You've shipped ML systems end-to-end and at scale: you design, build and test reliable, scalable ML data pipelines; you know how to explore and manipulate 3D point-cloud and mesh data to enable geometry-aware modelling; you select the right libraries, frameworks and tools. Working at the intersection of data science and software engineering, you translate R&D and project outputs into reusable libraries, tooling and products.
With at least 2 years industry experience (post Masters or PhD) in a commercial, non-research environment. You're truly excited about taking ownership of complex work streams and guiding teams to success, while continuously improving the systems and solutions you work on to ensure they are practical, impactful and meet the evolving needs of our customers.

We have hybrid offices in London, New York, and Singapore; this role is hybrid based in the San Francisco area.

This Role 

As a Machine Learning Engineer, you'll work closely with our Data Scientists, Simulation Engineers, and customers to understand and define the engineering and physics challenges we are solving. You will iterate with customers and use your influence to drive decisions around reliable deployment with measurable outcomes.

What you will do
  • Work closely with our simulation engineers, data scientists and customers to develop an understanding of the physics and engineering challenges we are solving
  • Design, build and test data pipelines for machine learning that are reliable, scalable and easily deployable
  • Explore and manipulate 3D point cloud & mesh data
  • Own the delivery of technical workstreams
  • Create analytics environments and resources in the cloud or on premise, spanning data engineering and science
  • Identify the best libraries, frameworks and tools for a given task, make product design decisions to set us up for success
  • Work at the intersection of data science and software engineering to translate the results of our R&D and projects into re-usable libraries, tooling and products
  • Continuously apply and improve engineering best practices and standards and coach your colleagues in their adoption
You'll also have the opportunity to travel to customer sites in North America, Europe, Asia, Oceania, for an average of 3-4 weeks per quarter, where you'll collaborate closely with customers to build solutions on-site.
 
What you bring to the table
  • Experience applying Machine learning methods (including 3D graph/point cloud deep learning methods) to real-world engineering applications, with a focus on driving measurable impact in industry settings.
  • Experience in ML/Computational statistics/Modelling use-cases in industrial settings (for example supply chain optimisation or manufacturing processes) is encouraged.
  • A track record of scoping and delivering projects in a customer facing role
  • 2+ years' experience in a data-driven role, with exposure to software engineering concepts and best practices (e.g., versioning, testing, CI/CD, API design, MLOps)
  • Building machine learning models and pipelines in Python, using common libraries and frameworks (e.g., TensorFlow, MLFlow)
  • Distributed computing frameworks (e.g., Spark, Dask)
  • Cloud platforms (e.g., AWS, Azure, GCP) and HP computing
  • Containerization and orchestration (Docker, Kubernetes)   
  • Strong problem-solving skills and the ability to analyse issues, identify causes, and recommend solutions quickly
  • Excellent collaboration and communication skills - with teams and customers alike
  • A background in Physics, Engineering, or equivalent
Our delivery teams drive innovation to turn AI models into practical solutions - read our blog to learn more about how you'll contribute to this exciting journey! What we offerBuild what actually matters

Help shape an AI-native engineering company at a formative stage, tackling problems that genuinely matter for industry and society. This is work with real-world impact - and something you can be proud to stand behind.

Learn alongside exceptional people

Work with a high-caliber, collaborative team of engineers, scientists, and operators who care deeply about doing great work, and about helping each other get better. We come from diverse backgrounds, but we share a commitment to operating at the highest level and addressing some of the most complex challenges out there. If you're ambitious, thoughtful, and driven by impact, you'll feel at home.

Influence over hierarchy

We operate with a flat structure: good ideas win - wherever they come from. Questioning assumptions and challenging the status quo isn't just welcomed, it's expected.

And it doesn't stop there ...

 Equity options - share meaningfully in the company you're helping to build.

 5% contribution to 401(k) - build long-term security with a strong retirement plan.

 Private health insurance - comprehensive cover for you, offering total peace of mind.

 Enhanced parental leave - 3 months full pay paternity and 6 months full pay maternity leave, to provide extra flexibility during the moments that matter most.

 20 days of Annual Leave (+ Public Holidays) - because taking time to rest matters.

 Personal development - dedicated support for learning, development, and leveling up over time.

 Gympass / Wellhub (subsidized) - for you and up to 3 family members, supporting both physical and mental wellbeing.

 Flexible Spending Account (FSA) - set aside pre-tax dollars for eligible healthcare expenses.

Watch this space, we're continuing to build this as we grow...

Salary range:

$150,000 - $190,000 depending on experience 
Seniority will be assessed throughout our interview process