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Machine Learning Engineer Jobs in Antioch, CA (NOW HIRING)

Walmart

(USA) Staff, Machine Learning Engineer

Hayward, CA · On-site

$169K - $338K/yr

The Staff Machine Learning Engineer will lead the design and delivery of scalable, production-grade ML solutions that address critical business needs. This role involves overseeing the end-to-end ML ...

Workday, Inc.

Senior/Principal AI Engineer

Pleasanton, CA · On-site

$140 - $180/hr

## Senior/Principal Machine Learning EngineerApplyremote type: Flexlocations: USA, CA, Pleasanton: USA ... Engineering, but brighter.**About the Role**As a Senior/Principal Machine Learning Engineer in ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA

You will join the Bioresilience Incubator, a dynamic engineering center that integrates predictive computational modeling, machine learning, and experimental biology to advance national security and ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

$9.7K/wk

You will join the Bioresilience Incubator, a dynamic engineering center that integrates predictive computational modeling, machine learning, and experimental biology to advance national security and ...

LLNL

Machine Learning (ML) Bioengineer

Livermore, CA · On-site

You will join the Bioresilience Incubator, a dynamic engineering center that integrates predictive computational modeling, machine learning, and experimental biology to advance national security and ...

LLNL

Machine Learning Researcher

Livermore, CA

Actively participate with project scientists and engineers in defining, planning, and formulating ... Adapt current machine learning research to real world applications at scale, with potentially ...

New

LLNL

Machine Learning Researcher

Livermore, CA · On-site

Actively participate with project scientists and engineers in defining, planning, and formulating ... Adapt current machine learning research to real world applications at scale, with potentially ...

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Showing results 21-40

All JobsMachine Learning Engineer JobsMachine Learning Engineer Jobs in Antioch, CA

Machine Learning Engineer information

See Antioch, CA salary details

$35.8K

$146.4K

$220K

How much do machine learning engineer jobs pay per year?

As of Aug 22, 2026, the average yearly pay for machine learning engineer in Antioch, CA is $146,407.00, according to ZipRecruiter salary data. Most workers in this role earn between $115,400.00 and $176,200.00 per year, depending on experience, location, and employer.

What is a machine learning engineer?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

What does a machine learning engineer do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What are the key skills and qualifications needed to thrive as a machine learning engineer, and why are they important?

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

What are some common challenges faced by machine learning engineers when deploying models to production?

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

What is the difference between Machine Learning Engineer vs Data Scientist?

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs

What job categories do people searching Machine Learning Engineer jobs in Antioch, CA look for?

The top searched job categories for Machine Learning Engineer jobs in Antioch, CA are:

What cities near Antioch, CA are hiring for Machine Learning Engineer jobs?

Cities near Antioch, CA with the most Machine Learning Engineer job openings:

Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Antioch, CA as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 25% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $146,407 per year, or $70.4 per hour.

Machine Learning Engineer - Reinforcement Learning

pony.ai

Fremont, CA • On-site

$150K - $250K/yr

Full-time

Medical, Dental, Vision, Life, Retirement, PTO

Re-posted 25 days ago

Job description

Founded in 2016 in Silicon Valley, Pony.ai has quickly become a global leader in autonomous mobility and is a pioneer in extending autonomous mobility technologies and services at a rapidly expanding footprint of sites around the world. Operating Robotaxi, Robotruck and Personally Owned Vehicles (POV) business units, Pony.ai is an industry leader in the commercialization of autonomous driving and is committed to developing the safest autonomous driving capabilities on a global scale. Pony.ai’s leading position has been recognized, with CNBC ranking Pony.ai #10 on its CNBC Disruptor list of the 50 most innovative and disruptive tech companies of 2022. In June 2023, Pony.ai was recognized on the XPRIZE and Bessemer Venture Partners inaugural “XB100” 2023 list of the world’s top 100 private deep tech companies, ranking #12 globally. As of August 2023, Pony.ai has accumulated nearly 21 million miles of autonomous driving globally. Pony.ai went public at NASDAQ in November 2024.

Responsibility
  • Build scalable systems for training and fine-tuning large generative models that produce realistic, informative driving behaviors for evaluation and scenario coverage.
  • Implement and iterate on RL-style methods: algorithms, reward / preference objectives, and training setups suited to high-fidelity, insightful behaviors in simulation-aligned workflows (closed-loop evaluation mindset).
  • Ship deep learning solutions (including LLM / VLM where appropriate) that improve human-led triaging, automate high-volume workflows, and support nuanced analysis of self-driving behavior to surface critical anomalies.
  • Own production-oriented ML for fleet-scale assessment: training, optimization, monitoring, and iteration of models used to judge performance across large real-world exposure.
  • Design and evolve data + evaluation systems inspired by RL from human preferences (RLHF) and related paradigms—turning preference/judgment signals into repeatable, scalable training and evaluation loops.
  • Partner broadly with teams such as Prediction, Planning, Research, and platform/engineering leads to land cross-cutting improvements with clear metrics.

Requirements

  • M.S. or Ph.D. in Computer Science, Machine Learning, AI, or a related field—or equivalent practical experience.
  • Hands-on experience building and applying ML in production-grade settings, with a strong RL component (policy learning, preference/feedback optimization, or offline/online RL pipelines).
  • Depth in deep learning, sequence modeling, and generative models.
  • Demonstrated impact via strong publications or a clear history of shipping impactful ML systems end-to-end.
  • Experience with large-scale distributed training and large-scale data processing.
  • Ability to lead ambiguous technical work from problem framing through reliable delivery.


Preferred
  • Background in autonomous vehicles, robotics, or complex simulation environments.
  • Strong grasp of modern RL and post-training techniques in LLM, dLLM, VLA and video generations.
  • Hands-on integration of simulation platforms with ML training and evaluation workflows.
  • Python fluency and frameworks such as PyTorch
  • Experience defining and operating metrics for complex, safety-critical AI systems.
  • Technical leadership: influencing stakeholders, aligning teams, and raising the bar for evaluation rigor.
  • Excellent communication—simple explanations of complex trade-offs.


Compensation and Benefits

Base Salary Range: $150,000 - $250,000 Annually

Compensation may vary outside of this range depending on many factors, including the candidate’s qualifications, skills, competencies, experience, and location. Base pay is one part of the Total Compensation and this role may be eligible for bonuses/incentives and restricted stock units.

Also, we provide the following benefits to the eligible employees:

  • Health Care Plan (Medical, Dental & Vision)
  • Retirement Plan (Traditional and Roth 401k)
  • Life Insurance (Basic, Voluntary & AD&D)
  • Paid Time Off (Vacation & Public Holidays)
  • Family Leave (Maternity, Paternity)
  • Short Term & Long Term Disability
  • Free Food & Snacks

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