# Machine Learning Engineer at Sift

- Company: Sift
- What the company does: Sift's fraud prevention platform stops payment fraud and account takeover in real time - with transparent workflows, global data and expert analysts. Backed by Insight.
- Company website: https://siftscience.com/
- Type: Startups (AI role)
- Level: Mid level
- Location: San Francisco, California
- Work setup: Remote
- Pay: $140K to $190K base salary per year (USD)
- Posted: 2026-07-20
- Apply by: 2026-10-08
- Apply: https://jobs.ashbyhq.com/sift/45b22605-1abb-483e-8ef7-5ceaf04f5868
- Page: https://www.1752.vc/careers/jobs/sift-machine-learning-engineer/

## About 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 they're looking for

- 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)
- Experience explicitly in the fraud detection, risk mitigation, or cyber-security domains

Tags: Engineering