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Remote Google Machine Learning Engineer Jobs in Richmond, CA

Elicit

Machine Learning Engineer

Oakland, CA · On-site +1

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

We invite you to help us build that future. (See how people use Elicit today on Twitter; explore our vision in the roadmap.) About the role As a Machine Learning Engineer at Elicit, you'll build ...

EvenUp

Staff Machine Learning Engineer

San Francisco, CA · Remote

$212K - $301K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Join EvenUp as a Staff Machine Learning Engineer and help set the technical direction for how ... Open to remote candidates or 3 days a week hybrid from our Toronto or San Francisco hubs. Benefits ...

Sprinter Health

Machine Learning Engineer

San Francisco, CA · Remote

$140K - $200K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

About the Role We're looking for an ML Engineer to build the production systems that train, deploy, monitor, retrain, and serve our machine-learning models reliably. You sit between software ...

EvenUp

Senior Machine Learning Engineer

San Francisco, CA · Remote

$196K - $265K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Senior Machine Learning Engineer At EvenUp, we leverage cutting-edge AI to bring fairness and accessibility to the legal system. Tackling the most complex legal document challenges requires expertise ...

Sprinter Health

Machine Learning Engineer (Staff)

San Francisco, CA · Remote

$220K - $270K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Staff Machine Learning Engineer About Sprinter Health At Sprinter Health, our mission is reimagining how people access care by bringing it directly to their homes. Nearly 30% of patients in the U.S ...

Care.coach

Lead Machine Learning Engineer

Millbrae, CA · On-site +1

$119K - $156K/yr

... key engineering leadership role -- Minimum Requirements: * Doctorate in a related field * 8+ years of experience (including any applicable work in grad school) developing machine learning ...

PlacingIT

Head of Machine Learning - 1811

San Francisco, CA · Remote

$180K - $250K/yr

Remote (United States) Employment Type: Direct Hire - Full-Time Compensation: $180K-$250K - based ... Partner closely with engineering, product, and executive leadership to define technical strategy ...

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All JobsGoogle Machine Learning Engineer JobsRemote Google Machine Learning Engineer JobsRemote Google Machine Learning Engineer Jobs in Richmond, CA

Remote Google Machine Learning Engineer information

See Richmond, CA salary details

$36.2K

$147.8K

$222.1K

How much do remote google machine learning engineer jobs pay per year?

As of Aug 15, 2026, the average yearly pay for remote google machine learning engineer in Richmond, CA is $147,785.00, according to ZipRecruiter salary data. Most workers in this role earn between $116,500.00 and $177,900.00 per year, depending on experience, location, and employer.

What is a remote Google machine learning engineer?

A Remote Google Machine Learning Engineer is a professional who designs, builds, and deploys machine learning models and artificial intelligence solutions, often using Google Cloud technologies, while working from a remote location. These engineers collaborate with cross-functional teams to solve complex business problems, optimize data pipelines, and improve model performance. Their responsibilities typically include data preprocessing, model selection, training, evaluation, and deployment, all while ensuring scalability and security. Working remotely allows them to contribute to projects from anywhere, leveraging cloud-based tools and collaboration platforms.

What are the key skills and qualifications needed to thrive as a remote Google machine learning engineer?

To thrive as a Remote Google Machine Learning Engineer, you need a strong background in computer science, mathematics, and machine learning algorithms, typically supported by a relevant degree and experience in building scalable models. Proficiency with tools such as TensorFlow, Python, Google Cloud Platform (GCP), and familiarity with distributed systems is essential. Excellent problem-solving, communication, and self-management skills are crucial for effective remote collaboration and innovation. These capabilities enable engineers to deliver impactful machine learning solutions while seamlessly integrating with global Google teams.

How do remote Google machine learning engineers typically collaborate with cross-functional teams while working from different locations?

Remote Google Machine Learning Engineers often use a combination of video conferencing, cloud-based collaboration tools, and shared code repositories to work closely with data scientists, product managers, and software engineers. Regular stand-up meetings, sprint planning sessions, and detailed documentation help ensure everyone is aligned and project milestones are met. Despite being remote, engineers are encouraged to proactively communicate progress, share insights, and participate in code reviews to maintain a strong team dynamic and drive successful project outcomes.

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

The top searched job categories for Remote Google Machine Learning Engineer jobs in Richmond, CA are:

What cities near Richmond, CA are hiring for Remote Google Machine Learning Engineer jobs?

Cities near Richmond, CA with the most Remote Google Machine Learning Engineer job openings:

Remote Google Machine Learning Engineer jobs near you

Machine Learning Engineer

Sift Science, Inc

San Francisco, CA • On-site, Remote

$140K - $190K/yr

Full-time

Re-posted 6 days ago

Job description

The Role:
As a Machine Learning Engineer at Sift, you will bridge the gap between data science and large-scale distributed systems. You won't just train models in isolation; you will build end-to-end pipelines that extract signals, train custom models per merchant, and serve predictions at production scale with low latency. You will work on an automated machine learning ecosystem that dynamically recalibrates models based on streaming global telemetry data.
What You'll Do:
  • Model Development & Refinement: Design, build, and deploy online machine learning models (including ensemble methods, deep learning, transformer architectures and graph-based models) to catch evolving fraud vectors in real time.
  • Feature Engineering at Scale: Engineer high-frequency time-series features from over 1 trillion behavioral events, optimizing for low-latency signal extraction and pattern recognition.
  • Production MLOps: Maintain and enhance our automated model training and deployment infrastructure, ensuring frictionless continuous integration and continuous deployment (CI/CD) of newly trained models.
  • System Optimization: Write high-performance code to minimize scoring latency at runtime, ensuring our core ML services scale seamlessly across distributed databases.
  • Collaborative Innovation: Work cross-functionally with Core Infrastructure, Product Management, and Data Science teams to translate business-level fraud patterns into robust algorithmic solutions.
What We Are Looking For (Requirements):
  • Experience: 4+ years of professional experience building and deploying large-scale machine learning models into high-traffic production environments.
  • Solid Programming Foundations: Strong proficiency in Java or Scala (for our production backend) as well as Python (for data analysis and model prototyping).
  • Distributed Systems & Big Data: Practical experience with Databricks and big data processing frameworks like Apache Spark, Apache Flink, or Hadoop, and working with NoSQL data stores like Bigtable.
  • Strong Mathematical Foundations: Deep understanding of statistical modeling, probability, and standard machine learning algorithms (e.g., XGBoost, Random Forests, Neural Networks, and Clustering techniques).
  • System Design Mentality: Ability to reason through data consistency, pipeline failures, and performance constraints in a distributed, multi-tenant cloud environment (GCP).
Bonus Points (Preferred Qualifications):
  • Experience explicitly in the fraud detection, risk mitigation, or cyber-security domains.
  • Deep knowledge of streaming architectures (e.g., Apache Kafka).
  • Familiarity with containerization and orchestration tools like Docker and Kubernetes.
  • Familiarity with leveraging AI coding assistants (e.g., Claude Code) to accelerate development and model prototyping

Please note: final stage candidates may be asked to travel for in-person final round interviews.
Let's build it together:
At Sift, we are intentionally building a diverse, equitable, and inclusive workplace. We believe that diversity drives innovation, equity is a fundamental right, and inclusion is a basic human need. We envision a place where all Sifties feel secure sharing their authentic selves and diverse experiences with their teams, their customers, and their community - ultimately using this empowerment and authenticity to build trust and create a safer Internet.
This document provides transparency around how Sift handles the personal data of job applicants: https://sift.com/recruitment-privacy
A little about us:
Sift is the AI-powered fraud platform securing digital trust for leading global businesses. Our deep investments in machine learning and user identity, a data network scoring 1 trillion events per year, and a commitment to long-term customer success empower more than 700 customers to grow fearlessly. Global brands rely on Sift to unlock growth and deliver seamless consumer experiences. Visit us at sift.com and follow us on LinkedIn.

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