Machine Learning Engineer
@ Apple

Cupertino, California, United States
$150,000
On Site
Full Time
Posted 14 hours ago

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Job Details

Overview

Apple's Applied Sensing & Health team is seeking a Machine Learning Engineer to innovate and enhance health and fitness features across Apple devices. Join a dynamic group responsible for developing sensor fusion algorithms that track human motion and deliver actionable insights.

What You Will Do

You will scope, design and implement cutting-edge models for Health and Fitness algorithms. In this role, you will:

  • Develop machine learning and deep learning models using time series data.
  • Optimize implementations for power, memory, and performance.
  • Collaborate with scientists, QA, engineers, and project managers.
  • Coordinate across multidisciplinary teams to deliver secure and scalable systems.
  • Ship features impacting millions of users daily.

Minimum Qualifications

MS degree with 3+ years in quantitative data science (statistics, biostatistics, epidemiology, computer science). Strong background in developing machine learning models with proficiency in Python and ML frameworks such as PyTorch and Tensorflow.

Preferred Qualifications

Ph.D or 5+ years in quantitative data science. Ability to form and test hypotheses, address computational and storage complexities and leverage distributed computing for large datasets. High importance on integrity of tooling and pipelines, and effective communication skills.

Key skills/competency

  • Machine Learning
  • Deep Learning
  • Time Series Analysis
  • Python
  • PyTorch
  • Tensorflow
  • Sensor Fusion
  • Data Science
  • Health Algorithms
  • Collaboration

How to Get Hired at Apple

🎯 Tips for Getting Hired

  • Research Apple: Understand their product innovation and culture.
  • Tailor your resume: Emphasize ML, Python, and sensor fusion experience.
  • Showcase projects: Highlight health and time series models.
  • Prepare for interviews: Practice technical and collaborative questions.

📝 Interview Preparation Advice

Technical Preparation

Review machine learning fundamentals and frameworks.
Practice coding in Python and ML algorithms.
Study sensor fusion techniques and time series data.
Test efficiency optimizations for power and memory.

Behavioral Questions

Describe a challenging project collaboration.
Explain how you handle project feedback.
Discuss a time you solved a complex problem.
Relate how you manage tight deadlines.

Frequently Asked Questions