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Machine Learning Data Engineer Jobs in California

ML Data Engineer San Francisco, CA · In Person · Full-Time Applying to this role will also allow ... Your job will be to help turn that trail into a machine. A design session isn't a simple sequence ...

Work with real-time data and content coming from various data sources. * Manage machine learning ... Strong programming skills in Python and Scala required. Experience in other programming languages ...

Work with real-time data and content coming from various data sources. * Manage machine learning ... Strong programming skills in Python and Scala required. Experience in other programming languages ...

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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 Sep 4, 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 is a machine learning data engineer?

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 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 machine learning data engineer become a machine learning engineer?

A machine learning data engineer can transition to a machine learning engineer role by developing skills in model development, algorithms, and deployment, often requiring knowledge of programming languages like Python and frameworks such as TensorFlow or PyTorch. Gaining experience in building and deploying models, along with understanding machine learning concepts, is essential for this career progression.

What is the salary of machine learning data engineer?

The salary of a machine learning data engineer typically ranges from $90,000 to $150,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in cloud platforms and big data tools may earn higher compensation.
Infographic showing various Machine Learning Data Engineer job openings in California as of August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 9% Part Time, and 2% Contract. Highlights an 85% Physical, 4% Hybrid, and 11% Remote job distribution, with an average salary of $128,018 per year, or $61.5 per hour.

Machine Learning & Data Engineer, Vehicle Modeling

42dot

Sunnyvale, CA • Hybrid

$136K - $163K/yr

Full-time

This job post has expired today. Applications are no longer accepted.


Job description

Machine Learning & Data Engineer, Vehicle Modeling

42dot is a mobility AI company committed to solving mobility challenges with software and AI. As the Global Software Center of Hyundai Motor Group, 42dot pioneers the future of mobility by advancing the development of software-defined vehicles.

We develop safety-first, user-centric software-defined vehicle technologies that deliver the latest performance through continuous updates like smartphones. By advancing software and AI technology, 42dot envisions a world where everything is connected and moves autonomously through a self-managing urban transportation operating system.

About the Role

We are building next-generation vehicle modeling and data technology at 42dot by combining physics-based models, machine learning, simulation, and large-scale vehicle data.

As a Machine Learning & Data Engineer, Vehicle Modeling, you will develop ML and data infrastructure that improves vehicle models and supports broader vehicle intelligence and autonomous driving development. You will work across vehicle telemetry, simulation, test data, fleet data, and cloud platforms to build scalable systems for model development, evaluation, and continuous improvement.

A core focus of this role is using real-world vehicle data to identify model performance gaps and improve models through calibration, parameter estimation, machine learning, and hybrid physics-and-data approaches. You may develop ML models that complement physics-based models, estimate model parameters under different vehicle states and operating conditions, or improve predictions where physical models alone are insufficient.

You will also help build the data and cloud infrastructure needed to ingest, clean, organize, process, and evaluate large-scale vehicle datasets, supporting modeling, simulation, vehicle intelligence, and autonomous driving workflows. This role sits at the intersection of machine learning, data engineering, physical system modeling, simulation, and vehicle software.

Responsibilities

  • Develop machine learning and hybrid physics-data approaches to improve vehicle and component model accuracy.
  • Develop methods for model calibration, parameter estimation, adaptive modeling, and data-driven model improvement across different vehicle states and operating conditions.
  • Build scalable workflows for comparing model predictions with test, simulation, and real-world vehicle data and identifying opportunities for model improvement.
  • Build pipelines for ingesting, cleaning, synchronizing, transforming, storing, and accessing large-scale vehicle telemetry and time-series data.
  • Develop cloud-based infrastructure for data processing, model training, simulation, evaluation, and validation.
  • Build tools and workflows for dataset management, model evaluation, experiment tracking, and reproducible model development.
  • Support data processing, model evaluation, and analysis workflows used by vehicle intelligence

Interview Process

  • Application Review - Coding Test - 1st interview - 2nd interview - Offer Negotiation - Hiring
  • The screening procedures may vary depending on the position, schedule, or other circumstances.
  • You will be individually notified of the screening schedule and results via the email address provided in your application.

Additional Information

  • In accordance with fair hiring practices, do not include any personal information unrelated to your job qualifications (e.g., Social Security Number, family relations, marital status, age, photo, physical condition, place of birth, etc.) in your resume.
  • All documents must be submitted in PDF format and under 30MB in size.
  • If you experience issues uploading your resume, please send it along with the job posting URL to recruit@42dot.ai.
  • We strongly encourage applications from U.S. veterans and candidates eligible for employment preference under applicable laws.
  • Qualified individuals with disabilities are encouraged to apply and will receive consideration under the Americans with Disabilities Act (ADA).
  • 42dot does not accept unsolicited resumes and will not pay fees for any such submissions. Equal Opportunity Statement
  • 42dot is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees, regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or veteran status.

※ Please review the following information before applying.

  • How to work in 42dot, About 42dot Way →