# Machine Learning Engineer

**Company:** [Sift](https://hotfix.jobs/companies/sift)
**Location:** San Francisco, CA, Seattle, WA
**Role:** ML Engineering
**Salary:** $140k – $190k/yr
**Experience:** 4+ years
**Skills:** Machine Learning, Python, Java, Scala, Databricks, Spark, apache flink, Hadoop, bigtable, xgboost, random forests, neural networks, GCP, apache kafka, Docker
**Posted:** 2026-07-20

> Build and deploy large-scale online ML models and end-to-end pipelines for real-time fraud detection at Sift, working on automated training systems that process over 1T events. Requires 4+ years production ML experience, strong Python/Java/Scala skills, and distributed systems expertise with Spark, Databricks, and GCP.

## Job Description

## 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.

## Requirements

- 4+ years of professional experience building and deploying large-scale machine learning models into high-traffic production environments.
- Strong proficiency in Java or Scala (for our production backend) as well as Python (for data analysis and model prototyping).
- Practical experience with Databricks and big data processing frameworks like Apache Spark, Apache Flink, or Hadoop, and working with NoSQL data stores like Bigtable.
- Deep understanding of statistical modeling, probability, and standard machine learning algorithms (e.g., XGBoost, Random Forests, Neural Networks, and Clustering techniques).
- Ability to reason through data consistency, pipeline failures, and performance constraints in a distributed, multi-tenant cloud environment (GCP).

## 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.

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