---
title: 'Machine Learning Engineer at Sift'
canonical: 'https://feeny.ai/job/machine-learning-engineer-sift-san-francisco-vw7hydmb2gs3'
type: 'job'
last_seen: '2026-09-08'
---

# Machine Learning Engineer at Sift

- **Company:** Sift
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-07-20
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/sift/45b22605-1abb-483e-8ef7-5ceaf04f5868

## 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 http://sift.com and follow us on LinkedIn https://www.globenewswire.com/Tracker?data=XHeK0v8NcNrEkwcDe8QxwpZeCkdQqNyKlni83U-CUmrprdKXWpVlYOAbVzwe2OmlwIUN-q4HXk4hf_dazpHx2NMM1CW_SYj740q9mxXNQI4=.

## About Sift

## Company Overview
- **One-liner**: Sift is an AI-powered fraud prevention platform that provides digital trust and safety solutions for global businesses.
- **Entity Type**: Private (late-stage, $156.6M total funding)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2011
- **Founders**: Jason Tan

## Core Business
- **Primary industry**: Digital fraud prevention, risk-based authentication, cybersecurity
- **Target customers**: B2B, enterprise, and mid-market digital businesses (e.g., e-commerce, fintech, on-demand services)
- **Mission / purpose**: To build a safer internet by helping companies stop fraud fast and grow revenue fearlessly

## Products & Services
- **[Sift AI-Powered Fraud Platform](https://sift.com)**: SaaS platform that uses machine learning and a global data network of 1 trillion annual events to detect and prevent account takeover, payment fraud, and first-party abuse in real time. Includes risk-based authentication, chargeback protection, and identity trust tools.

## Market Standing
- **Valuation / Market Cap**: Not publicly disclosed
- **Key Metric**: Annual revenue of $65.0M (LinkedIn estimate) and total funding of $156.6M across 8 rounds (LinkedIn)
- **Notable Investors/Partners**: Union Square Ventures (Albert Wenger serves on the board); customers include DoorDash, Yelp, Poshmark, Harry’s, Hertz, Patreon
- **Growth Signals**:  
  - Protects 700+ global brands and processes events from 34,000+ sites and apps  
  - Maintained #1 position across all Fraud Prevention categories in G2’s Fall 2025 Reports  
  - 40+ granted or allowed U.S. patents  
  - Released “Advanced Fraud Investigation Tooling” in Fall ’25 release

## Competitive Advantages
- **Data network effect**: Over 1 trillion annual events feed Sift’s machine learning models, improving accuracy and speed as the network grows
- **AI-first platform**: Real-time decisioning at scale with automated rulesets and machine learning
- **Established brand trust**: 700+ enterprise customers, long-tenured relationships with market leaders
- **Patent portfolio**: 40+ patents covering fraud detection, identity trust, and risk-based authentication

## Strategic Focus
- **Digital trust & safety**: Deepening identity trust capabilities and simplifying fraud management for lean teams
- **Global expansion**: Scaling the data network and adding support for new regions and fraud types
- **Product innovation**: Investing in advanced investigation tooling, user-centric insights, and automation to help customers turn risk into revenue

## Why Work Here
- **Culture**: Values include “Ever Better,” “Win as One Team,” and “Courage Over Comfort”; emphasis on candid feedback, vulnerability, and leaving your comfort zone
- **Work model**: “Borderless Sift” – hybrid for Bay Area employees (flexible in-office), fully remote for others; designed to support execution excellence and belonging
- **Benefits**:  
  - Generous time off, mental health days, paid maternity/paternity leave  
  - Employee equity, retirement savings plan, lifestyle wallet  
  - Medical/dental/vision fully covered for individuals (affordable premiums for dependents)  
  - Learning & development wallet, in-house group workshops, manager trainings
- **Engineering culture**: Uses modern tech stack (Ruby, Java, Snowflake, Google Cloud); teams are empowered to scale fraud prevention with agility

## Sources
1. [Sift.com – Company Overview](https://sift.com/)
2. [Sift Careers Page](https://sift.com/careers/)
3. [Sift About/Leadership Page](https://sift.com/company/)
4. [Built In – Sift Company Profile](https://builtin.com/company/sift)
5. [LinkedIn – Sift Company Page](https://www.linkedin.com/company/getsift)

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