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Machine Learning Data Engineer Jobs in California

Lead Data Engineer

San Francisco, CA · On-site

$180K - $225K/yr

YOUR TEAM The Data org at Nuna is an interdisciplinary group spanning data science, machine learning, data analytics, actuarial science, and research. The Data Engineer team is a core part of the ...

Data Engineer

San Francisco, CA

$134K - $162K/yr

Competitive We are looking for a skilled Data Engineer with a strong focus on AI and machine learning to join our dynamic team. The ideal candidate will play a critical role in designing ...

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 ...

Magnopus is looking for a Machine Learning Engineer who thrives at the intersection of product ... Build pipelines and prepare data for tuning, training, and deployment of models optimized for ...

Magnopus is looking for a Machine Learning Engineer who thrives at the intersection of product ... Build pipelines and prepare data for tuning, training, and deployment of models optimized for ...

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 ...

You'llwork alongside experienced engineers, data scientists, and domain experts while gaining ... Machine Learning & Advanced Analytics * Develop and evaluate ML models (e.g., classification ...

Advantest is seeking a motivated Junior Machine Learning Engineer to support the development of data-driven and ML-powered solutions for semiconductor R&D, test, and operations teams. In this role ...

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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 6, 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.

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 engineers make $300,000 a year?

Senior machine learning data engineers with extensive experience, advanced skills in data pipelines, cloud platforms, and programming languages like Python or Scala can earn $300,000 or more annually. High compensation often reflects leadership roles, specialized expertise, or work at large tech companies and financial institutions.

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 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:
AI/Machine Learning Engineering - Intern

AI/Machine Learning Engineering - Intern

DataVisor

Mountain View, CA

$25 - $70/hr

Contractor

Posted 15 days ago


Job description

About DataVisor

DataVisor is the world's leading AI-powered Fraud and Risk Platform that delivers the best overall detection coverage in the industry. With an open SaaS platform that supports easy consolidation and enrichment of any data, DataVisor's fraud and anti-money laundering (AML) solutions scale infinitely and enable organizations to act on fast-evolving fraud and money laundering activities in real time. Its patented unsupervised machine learning technology, advanced device intelligence, powerful decision engine, and investigation tools work together to provide significant performance lift from day one. DataVisor's platform is architected to support multiple use cases across different business units flexibly, dramatically lowering total cost of ownership, compared to legacy point solutions. DataVisor is recognized as an industry leader and has been adopted by many Fortune 500 companies across the globe.

Our award-winning software platform is powered by a team of world-class experts in big data, machine learning, security, and scalable infrastructure. Our culture is open, positive, collaborative, and results-driven. Come join us!

Role Summary

We are seeking highly motivated, soon-to-graduate MS or Ph.D. students in Computer Science, Machine Learning, Data Science, or related fields to join us as AI / ML Engineering Interns.

This internship is ideal for candidates who are eager to learn how large-scale AI systems are built and deployed in production. You will work closely with experienced engineers and data scientists to help build the Intelligence Layer and Data Consortium that power DataVisor's real-time fraud detection platform. 

This internship focuses on distributed systems, data pipelines, machine learning infrastructure, and applied AI, including exposure to agentic flows and large language models (LLMs).

What You'll Do

  • Data Engineering & Pipelines
    • Assist in building and maintaining high-throughput data pipelines using technologies such as Spark, Kafka, or Flink
    • Help process and aggregate real-time signals (e.g., device fingerprints, behavioral data) into shared intelligence systems
  • Distributed Systems & Scalability
    • Learn to design and optimize backend systems that support large-scale, real-time decisioning
    • Contribute to improving system performance, reliability, and latency under high transaction volumes
  • AI Applications & Agentic Flows
    • Support the development of AI applications and agentic workflows using state-of-the-art LLMs (e.g., OpenAI, Anthropic, Google)
    • Experiment with natural language interfaces, intelligent rule suggestions, and automated strategy generation
  • Machine Learning Pipelines
    • Help deploy and monitor pipelines for unsupervised and supervised ML models
    • Assist with integrating models into real-time scoring APIs and decision engines
  • Privacy & Security
    • Learn best practices for privacy-first system design, including tokenization and hashing to protect sensitive data
  • Cross-Functional Collaboration
    • Work alongside Data Science, Product, and Engineering teams to test ideas, validate models, and ship production features

Requirements

  • Current MS or Ph.D. students majoring in Computer Science, Machine Learning, AI, Data Science, or a related field 
  • Passionate about learning how real-world AI systems are built at scale
  • Comfortable working with complex technical problems and eager to grow through mentorship
  • Strong programming skills in Python
  • Familiarity with at least one of the following: distributed systems, machine learning, data engineering, or backend development
  • Academic or project experience with big data frameworks (Spark, Kafka, Flink) is a plus
  • Understanding of core ML concepts (supervised / unsupervised learning)
Preferred (Nice-to-Have)
  • Coursework or project experience with:
    • LLMs, RAG architectures, LangChain, or vector databases
    • Cloud platforms (AWS) and containers (Docker)
    • Stream processing or real-time systems
  • Interest in fraud, risk, or security domains (not required)

Benefits

  • Hands-on experience working on production-scale AI systems
  • Mentorship from senior engineers and data scientists
  • Exposure to cutting-edge agentic AI and LLM applications
  • Opportunity for full-time conversion based on performance and business needs
  • Comp Range, $25 - $70/hour