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Research Assistant Machine Learning Jobs in California

Winaxis

Machine Learning Engineer

Fremont, CA · On-site

Machine Learning Engineer Location: Fremont, CA (Local) Onsite interview Duration: 12+ Mos H1B Only ... Write clean, modular, and sustainable code to translate research ideas into production-ready ...

Apple

Machine Learning Engineer

Sunnyvale, CA · On-site

The Machine Learning Platform Technology team is building groundbreaking technology for search ... Our infrastructure and research for data curation form the backbone of Apple Intelligence. It ...

S27a

Machine Learning Engineer

San Francisco, CA · On-site

$150 - $200/hr

Research, design, and implement machine learning algorithms to optimize workflow automation. * Develop, test, and modify computer programs to apply machine learning models to real-world applications.

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA

We have an opening for a Machine Learning (ML) Bioengineer to conduct research training and evaluating next-generation clinical, protein and genome language models. You will join the Bioresilience ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

Wehave an opening for a Machine Learning (ML) Bioengineer to conduct research training and evaluating next-generation clinical, protein and genome language models. You will join the Bioresilience ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

$9.7K/wk

We have an opening for a Machine Learning (ML) Bioengineer to conduct research training and evaluating next-generation clinical, protein and genome language models. You will join the Bioresilience ...

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Research Assistant Machine Learning information

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How much do research assistant machine learning jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for research assistant machine learning in California is $21.62, according to ZipRecruiter salary data. Most workers in this role earn between $18.27 and $25.14 per hour, depending on experience, location, and employer.

What is a research assistant machine learning?

A Research Assistant in Machine Learning supports research projects by implementing algorithms, analyzing data, and conducting experiments to advance AI models. They assist senior researchers by preprocessing datasets, developing machine learning models, and evaluating their performance. Responsibilities may also include coding, literature reviews, and writing research papers. This role is typically found in academia, research labs, or industry R&D teams. Strong programming skills, statistical knowledge, and familiarity with ML frameworks like TensorFlow or PyTorch are essential.

What types of projects might a research assistant machine learning typically work on?

As a Research Assistant in Machine Learning, you may be involved in projects such as developing and evaluating predictive models, processing and analyzing large datasets, and assisting in the publication of research findings. Your work could contribute to applications like natural language processing, computer vision, or recommendation systems, depending on the focus of the research group. You’ll often collaborate closely with senior researchers, data scientists, or PhD students, allowing you to participate in brainstorming sessions, code development, and experimental design. This experience provides valuable exposure to cutting-edge technology and can serve as a strong foundation for a research or industry career in machine learning.

What are the key skills and qualifications needed to thrive as a research assistant machine learning?

To thrive as a Research Assistant Machine Learning, you need a solid understanding of machine learning algorithms, programming skills (especially in Python or R), and a background in statistics or computer science, often supported by a bachelor’s or master’s degree. Experience with frameworks such as TensorFlow, PyTorch, and data analysis tools, as well as familiarity with version control systems like Git, is highly beneficial. Strong problem-solving abilities, attention to detail, and effective communication skills help you excel in collaborative research environments. These skills ensure you can contribute meaningfully to research projects, analyze complex datasets, and communicate findings effectively within interdisciplinary teams.

What are popular job titles related to Research Assistant Machine Learning jobs in California?

For Research Assistant Machine Learning jobs in California, the most frequently searched job titles are:

What job categories do people searching Research Assistant Machine Learning jobs in California look for?

The top searched job categories for Research Assistant Machine Learning jobs in California are:

What cities in California are hiring for Research Assistant Machine Learning jobs?

Cities in California with the most Research Assistant Machine Learning job openings:

Research Assistant Machine Learning jobs near you
Infographic showing various Research Assistant Machine Learning job openings in California as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 59% Full Time, 38% Part Time, and 1% Contract. Highlights an 82% Physical, 3% Hybrid, and 15% Remote job distribution, with an average salary of $44,974 per year, or $21.6 per hour.

Machine Learning Engineer

Fremont, CA • On-site

Winaxis
IT Services • 11 - 50 employees
Learn more about Winaxisexternal

Contractor

Re-posted 24 days ago

Job description

Title: Machine Learning Engineer

Location: Fremont, CA (Local) Onsite interview

Duration: 12+ Mos  

H1B

Only h1 candidate

About the Role:

Our direct client is hiring a Machine Learning Engineer for their software machine learning and computer vision team to design, develop, and implement critical machine learning models supporting factory and warehouse operations. You will transform ambiguous problem statements into robust end-to-end solutions using a variety of machine learning techniques and tools, including supervised learning, convolutional neural networks, and modern frameworks such as PyTorch and Pandas.

You will collaborate closely with partners in production, process, controls, and quality to deliver solutions for the most challenging problems in our operations. Your work will involve evaluating and deploying models in production environments, ensuring rapid and reliable alerting systems, and addressing operational issues as they arise. You must be adept at handling diverse, heterogeneous datasets that span multiple modalities, including images, multi-spectral sensor outputs, voice, text, and tabular data.

Responsibilities

Design, develop, and deploy machine learning models for factory and warehouse environments.

Collaborate with cross-functional teams to identify, define, and solve high-impact operational challenges.

Build and maintain end-to-end machine learning pipelines, from data collection and preprocessing to model deployment and monitoring.

Evaluate and compare models using statistical methods to ensure optimal performance and feasibility.

Ensure robust alerting and monitoring systems are in place for deployed models to address issues rapidly.

Work with diverse datasets, integrating multiple data types such as images, sensor data, voice, text, and tabular information.

Write clean, modular, and sustainable code to translate research ideas into production-ready solutions.

Minimum Requirements

In-depth knowledge of Python for high-performance, data-intensive applications.

Proficiency with at least one modern deep learning framework (e.g., PyTorch, Jax, TensorFlow).

Expertise in one or more of the following areas: computer vision, large language models, recommender systems, or operations research.

Foundational knowledge of statistics for model comparison and performance assessment.

Real-world experience deploying and maintaining machine learning solutions in production environments.

Passion for clean, sustainable, and modular code to bring research concepts to practical implementation.

Preferred Qualifications

Experience working in manufacturing, industrial automation, or warehouse environments.

Familiarity with multi-modal data integration and analysis.

Strong problem-solving skills and the ability to thrive in ambiguous, fast-paced settings.

Excellent communication skills for cross-functional teamwork.