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Machine Learning Data Engineer Jobs in California

IT America Inc

Sr. Data Engineer (AI + AWS)

Irvine, CA · On-site

$122K - $147K/yr

Position: Sr. Data Engineer (AI + AWS) Location: Irvine/LA, CA (Onsite) Duration: Long term ... machine learning data infrastructure. The ideal candidate will have experience building cloud ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

Abaka AI is built on a mission to be the world's most trusted data partner for AI companies. They are seeking a Machine Learning Engineer to design and develop scalable training pipelines for ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

Abaka AI is built on the mission to be the world's most trusted data partner for AI companies, supporting global partners with reliable and scalable data solutions. The Machine Learning Engineer will ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

$175K - $275K/yr

Whether teams need raw data, curated datasets, or full-cycle data engineering, Abaka AI provides ... About the Role We're hiring our first Machine Learning Engineer in the United States, a ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

$175K - $275K/yr

Whether teams need raw data, curated datasets, or full-cycle data engineering, Abaka AI provides ... About the Role We're hiring our first Machine Learning Engineer in the United States, a ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

$175K - $275K/yr

Whether teams need raw data, curated datasets, or full-cycle data engineering, Abaka AI provides ... About the Role We're hiring our first Machine Learning Engineer in the United States, a ...

Abaka AI

Machine Learning Engineer

Mountain View, CA · On-site

$175K - $275K/yr

Whether teams need raw data, curated datasets, or full-cycle data engineering, Abaka AI provides ... About the Role We're hiring our first Machine Learning Engineer in the United States, a ...

Machina Labs

$160 - $190/hr

We are looking for a Machine Learning Engineer to join our team and help us push the boundaries of ... You'll work closely with our engineering team to transform raw data into actionable intelligence ...

New

Machina Labs

Machine Learning Engineer

Chatsworth, CA

$160K - $190K/yr

We are looking for a Machine Learning Engineer to join our team and help us push the boundaries of ... You'll work closely with our engineering team to transform raw data into actionable intelligence ...

Machina Labs

Machine Learning Engineer

Chatsworth, CA · On-site

$160K - $190K/yr

We are looking for a Machine Learning Engineer to join our team and help us push the boundaries of ... You'll work closely with our engineering team to transform raw data into actionable intelligence ...

PhysicsX

Machine Learning Engineer

San Francisco, CA · On-site

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 ...

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Showing results 41-60

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 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.

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 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.

Sr. Data Engineer (AI + AWS)

IT America Inc

Irvine, CA • On-site

$122K - $147K/yr

Contractor

Re-posted 20 days ago

Job description

Position: Sr. Data Engineer (AI + AWS)

Location: Irvine/LA, CA  (Onsite)

Duration: Long term contract

Job Summary:

We are seeking a highly skilled Data Engineer with expertise in AI-enabled data platforms, AWS cloud services, Python, PySpark, and Kubernetes to design, develop, and optimize scalable data pipelines and machine learning data infrastructure. The ideal candidate will have experience building cloud-native data solutions, processing large-scale datasets, and supporting AI/ML workloads in AWS environments.

Key Responsibilities:

  • Design, build, and maintain scalable ETL/ELT data pipelines using Python and PySpark.
  • Develop cloud-native data solutions utilizing AWS services such as S3, EMR, Glue, Lambda, Redshift, Athena, ECS/EKS, IAM, CloudWatch, and Step Functions.
  • Build and optimize data ingestion frameworks for structured, semi-structured, and streaming data.
  • Collaborate with Data Scientists and AI Engineers to prepare, transform, and deliver high-quality datasets for AI/ML model training and inference.
  • Deploy and manage containerized data applications using Kubernetes (EKS) and Docker.
  • Develop data processing workflows using Spark and optimize performance for large-scale distributed processing.
  • Design data lakes and modern data architectures following AWS best practices.
  • Implement data quality checks, monitoring, logging, and alerting mechanisms.
  • Optimize SQL queries and data models for analytical workloads.
  • Build CI/CD pipelines for automated deployment of data engineering solutions.
  • Ensure data governance, security, compliance, and access controls across cloud environments.
  • Troubleshoot production issues and provide performance tuning for distributed data systems.
  • Work closely with cross-functional teams in Agile/Scrum environments.

Required Qualifications:

  • Bachelor's or Master's degree in Computer Science, Information Technology, Data Engineering, or related field.
  • 10+ years of Data Engineering experience.
  • Strong programming experience in Python.
  • Hands-on expertise with PySpark and Apache Spark.
  • Strong experience with AWS Cloud services.
  • Experience with Kubernetes (EKS) and Docker.
  • Strong SQL skills and experience with relational databases.
  • Experience building scalable ETL/ELT pipelines.
  • Familiarity with Git and CI/CD practices.
  • Excellent analytical, debugging, and problem-solving skills.

Required Technical Skills:

  • Cloud: AWS (S3, Glue, EMR, Lambda, Redshift, Athena, ECS/EKS, IAM, CloudWatch, Step Functions)
  • Programming: Python
  • Big Data: PySpark, Apache Spark
  • Containers: Kubernetes, Docker
  • Databases: PostgreSQL, MySQL, SQL Server, Redshift
  • Data Storage: Data Lake, Data Warehouse
  • Version Control: Git
  • Operating Systems: Linux
  • Methodology: Agile/Scrum

AI/ML Experience:

  • Support AI/ML data pipelines and feature engineering.
  • Prepare datasets for model training and inference.
  • Experience integrating ML workflows into cloud-based data platforms.
  • Familiarity with LLMs, Generative AI, Vector Databases, or Retrieval-Augmented Generation (RAG) is a plus.
  • Experience with AWS AI services such as Amazon SageMaker, Bedrock, or Amazon OpenSearch is preferred.

Preferred Qualifications:

  • Experience with Apache Airflow or AWS Managed Workflows (MWAA).
  • Knowledge of Kafka or Kinesis for streaming data.
  • Experience with Delta Lake, Iceberg, or Apache Hudi.
  • Infrastructure-as-Code experience using Terraform or CloudFormation.
  • AWS certifications (Solutions Architect, Data Engineer, or Machine Learning Specialty) are highly desirable.