Machine Learning Engineer – Fraud Engineering, Algorithms, and Risk


Job Overview

Key Qualifications

  • *Must Have:*
  • Excellent social, written, and verbal communication skills
  • Curiosity, passion for learning, high personal integrity, and a dedication to improving the Apple customer experience
  • Established programming skills in Scala, Java, Python, or similar language
  • Working knowledge of machine learning algorithms including classifiers, clustering algorithms, or anomaly detection
  • Experience with end-to-end ownership of projects (including academic projects) and confidence to make key decisions independently
  • *Nice to Have:*
  • 2-4 years industry experience after graduation, or multiple industry internships
  • Experience with SQL, Spark, or Hive
  • Experience working on distributed systems
  • Software development experience in industry, especially with source code management tools like git
  • Experience collaborating with other engineering and non-engineering teams
  • Description

    The role requires software engineering expertise to develop and deploy highly scalable algorithms that are used by hundreds of millions of users every day across the globe. We collaborate with other engineers, feature teams, and business partners across a wide range of groups within Apple to identify problems, define solutions, execute plans, measure results and communicate these results to our partners. We have end-to-end responsibility for our solutions to these problems, from initial investigation, to solution proposal, to implementation, to long-term maintenance. A successful candidate will have a bent for applied research with a broad knowledge of machine learning and expertise in anomaly detection, predictive modeling, classification and optimization. We encourage our team to stay abreast of current research by attending conferences. We cultivate a collaborative work environment, but allow solution autonomy on projects.

    Education & Experience

    BS Computer Science, Statistics, Mathematics, a degree in a related field or relevant work experience.Candidates with related work experience, including industry internships, are preferred.

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