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Online Machine Learning Jobs in California (NOW HIRING)

NTENT

Machine Learning Engineer

Carlsbad, CA · On-site

Machine learning, natural language processing, learning-to-rank, online learning, deep learning, interactive machine learning, machine teaching, conversational agents, human computer interaction ...

NTENT

Machine Learning Engineer

Carlsbad, CA

Machine learning, natural language processing, learning-to-rank, online learning, deep learning, interactive machine learning, machine teaching, conversational agents, human computer interaction ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site

Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...

Tapestry

Machine Learning Engineer

Mountain View, CA · On-site

About the role: We're looking for an early career Machine Learning Engineer to join our team. In this role you will build and deploy state of the art machine learning models to solve complex ...

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site

Company Description PatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems. We're seeking an outstanding ML Engineer to join our data ...

Winaxis

Machine Learning Engineer

Fremont, CA · On-site

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 ...

MM International

Machine Learning Engineer

Fremont, CA · On-site

Machine Learning Engineer Location: Fremont, CA once the documents are verified, a Codility assessment will be shared with the candidate, where they need to score a minimum of 70% and post that, a ...

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All JobsOnline Machine Learning JobsOnline Machine Learning Jobs in California

Online Machine Learning information

See California salary details

$25.2K

$42K

$86.8K

How much do online machine learning jobs pay per year?

As of Sep 3, 2026, the average yearly pay for online machine learning in California is $42,026.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,100.00 and $45,400.00 per year, depending on experience, location, and employer.

What is online machine learning?

Online machine learning is a method where models are trained incrementally as new data becomes available, rather than being trained all at once on a fixed dataset. This approach is particularly useful in environments where data arrives continuously, such as real-time analytics, recommendation systems, and fraud detection. Online learning algorithms update their knowledge with each new data point, allowing them to adapt quickly to changes and trends. This makes them ideal for applications that require immediate responses and adaptability to evolving data streams.

What are the key skills and qualifications needed to thrive as an online machine learning engineer?

To excel as an Online Machine Learning Engineer, you need a strong background in computer science, statistics, and machine learning algorithms, often supported by a relevant degree and experience with streaming data. Familiarity with tools such as Apache Kafka, Spark Streaming, Python, TensorFlow, and real-time data processing frameworks is critical. Problem-solving ability, adaptability, and effective communication are essential soft skills for collaborating with multidisciplinary teams and responding to rapidly changing data. These competencies are crucial for building scalable, responsive models that provide timely insights in dynamic production environments.

How does collaboration typically work between online machine learning engineers and data scientists in a project setting?

Online machine learning engineers often work closely with data scientists to ensure that the models they develop can be effectively deployed and updated in real-time environments. While data scientists may focus on feature engineering, model selection, and initial training using historical data, online machine learning engineers are responsible for integrating these models into production systems and implementing mechanisms for continuous learning from live data streams. Regular meetings, code reviews, and shared documentation are common practices to facilitate smooth collaboration and ensure that the models remain accurate and efficient as new data arrives.

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

AspectOnline Machine LearningData Scientist
Required CredentialsBachelor's or master's in CS, ML, or related fields; certifications in ML or data analysisBachelor's or master's in CS, statistics, or related fields; advanced degrees often preferred
Work EnvironmentTech companies, startups, research labs; focus on real-time data processingCorporate, consulting, or research settings; focus on data analysis and modeling
Industry UsageMachine learning applications, AI development, real-time systemsData analysis, predictive modeling, business insights

Online Machine Learning specialists focus on developing algorithms that learn continuously from streaming data, often in real-time environments. Data Scientists analyze large datasets to extract insights, build models, and support decision-making. While both roles require knowledge of machine learning, Online Machine Learning emphasizes real-time data processing, whereas Data Scientists focus on data analysis and modeling for strategic insights.

What are the most commonly searched types of Machine Learning jobs in California?

The most popular types of Machine Learning jobs in California are:

What cities in California are hiring for Online Machine Learning jobs?

Cities in California with the most Online Machine Learning job openings:

Online Machine Learning jobs near you
Infographic showing various Online Machine Learning job openings in California as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 18% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $42,026 per year, or $20.2 per hour.

Machine Learning Engineer

Sift Science, Inc

San Francisco, CA

$140K - $190K/yr

Full-time

Re-posted 24 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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