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Sift Jobs in California (NOW HIRING)

Sift

Machine Learning Engineer

San Francisco, CA · On-site

$160 - $230/hr

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 ...

Sift

Senior Security Engineer

San Francisco, CA · On-site

$140 - $210/hr

The Security Engineering team is responsible for protecting Sift's products, infrastructure, and data while enabling our engineering organization to ship quickly and safely. We embed with product and ...

Sift

Application Software Engineer

Marina Del Rey, CA · On-site

As a Forward Deployed Engineer, you will bridge the gap between Sift's telemetry platform and customer engineering challenges, developing custom solutions and fostering partnerships. Responsibilities ...

Sift

Full-Stack Engineer

San Francisco, CA · On-site

$120 - $190/hr

Because those requests can touch almost any part of the product, our work spans most of Sift's major technical domains and products. The requests we take on are typically high-impact and come ...

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Sift information

What is the difference between Sift vs Fraud Analyst?

AspectSiftFraud Analyst
CredentialsTypically requires knowledge of machine learning, data analysis, and fraud detection toolsOften requires experience in fraud prevention, investigation, and relevant certifications
Work EnvironmentTech-driven, often in e-commerce, SaaS, or online platformsFinancial institutions, e-commerce, or retail sectors
Employer & IndustryStartups, tech companies, online marketplacesBanks, credit card companies, online retailers
Search & Comparison IntentUnderstanding tech-based fraud detection solutionsInvestigating fraud prevention careers and roles

While both Sift and Fraud Analyst roles focus on preventing fraud, Sift is a technology platform utilizing machine learning to detect fraud automatically, often requiring technical skills. In contrast, a Fraud Analyst manually investigates suspicious activities, often within financial or retail sectors. The roles complement each other, with Sift providing automated solutions and Fraud Analysts performing detailed investigations.

What are popular job titles related to Sift jobs in California?

For Sift jobs in California, the most frequently searched job titles are:

What job categories do people searching Sift jobs in California look for?

The top searched job categories for Sift jobs in California are:

What cities in California are hiring for Sift jobs?

Cities in California with the most Sift job openings:

Sift jobs near you
Infographic showing various Sift job openings in California as of August 2026, with employment types broken down into 81% Full Time, 12% Part Time, 5% Contract, and 2% Nights. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Machine Learning Engineer

Sift

San Francisco, CA • On-site

$160 - $230/hr

Other

Re-posted 8 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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