Machine Learning Engineer

Sift

Machine Learning Engineer developing end-to-end pipelines for fraud detection at Sift. Collaborating across teams to build and deploy robust machine learning models at scale.

Posted 7/20/2026full-timeSan Francisco • California • 🇺🇸 United StatesMid-LevelSenior💰 $140,000 - $190,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying large-scale machine learning models, with a strong focus on optimizing performance in high-traffic production environments. Proficient in statistical modeling and big data processing, ensuring effective collaboration across cross-functional teams.

Highest-signal resume keywords

Machine Learning Model DeploymentJava or Scala ProficiencyPython for Data AnalysisDatabricks and Apache Spark ExperienceStatistical Modeling and Algorithms

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningStatistical ModelingDeep LearningEnsemble MethodsTransformer ArchitecturesGraph-Based ModelsHigh-Performance Code WritingTime-Series Feature EngineeringPattern RecognitionData Consistency Reasoning

Tools & Technologies

DatabricksApache SparkApache FlinkHadoopNoSQL Data StoresGCP

Industry Keywords

Continuous IntegrationContinuous DeploymentHigh-Traffic Production EnvironmentsBehavioral EventsMulti-Tenant Cloud Environment

Tech Stack

Tools & technologies

ApacheCloudGoogle Cloud PlatformHadoopJavaNoSQLPythonScalaSpark

About the role

Key responsibilities & impact

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.
Engineer high-frequency time-series features from over 1 trillion behavioral events, optimizing for low-latency signal extraction and pattern recognition.
Maintain and enhance our automated model training and deployment infrastructure, ensuring frictionless continuous integration and continuous deployment (CI/CD) of newly trained models.
Write high-performance code to minimize scoring latency at runtime, ensuring our core ML services scale seamlessly across distributed databases.
Work cross-functionally with Core Infrastructure, Product Management, and Data Science teams to translate business-level fraud patterns into robust algorithmic solutions.

Requirements

What you’ll need

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).

Benefits

Comp & perks

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