Spanish bilingual and Hispanic jobs since 1997. Diversity job fairs since 2006. employers     login   |   register - post a job
Hispanic Diversity Recruitment - best jobs for hispanic, latino & bilingual (spanish & portuguese) jobseekers
    Log me in!   |   Site Map   |   Help   
 Machine Learning Engineer - Fraud Engineering, Algorithms, and Risk - Cupertino, California, United States

Job information
Posted by: Apple 
Hiring entity type: Retail 
Work authorization: Not Specified for United States
Position type: Direct Hire, Full-Time 
Compensation: ******
Benefits: See below
Relocation: Not specified 
Position functions: Computers - Programming Languages
Travel: Unspecified 
Accept candidates: from anywhere 
Languages: English - Fluent
Minimum education: See below 
Minimum years experience: See below 
Resumes accepted in: English
Cover letter: No cover letter requested
Job code: 200251456 / Latpro-3795560 
Date posted: May-28-2021
State, Zip: California, 95014


Machine Learning Engineer - Fraud Engineering, Algorithms, and Risk

Santa Clara Valley (Cupertino) , California , United States

Software and Services


Posted: May 28, 2021

Role Number: 200251456

Imagine what you could do here. At Apple, new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish. Internet Software and Services is responsible for delivering cutting edge applications like the App Store, Apple Music and iCloud. Our team protects these Apple services and customers from fraud and abuse through a combination of threat modeling, data analysis, and machine learning. We are seeking a machine learning engineer with a drive to dig deep into how customers interact with these services and turn the huge amounts of data generated by these applications into feature improvements that enhance customer experience.

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


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.


See job description


Apple requires you to fill in their on-line form which will open in a different window.

Enter your email address and click 'Apply':
  Prefer not to enter your email?