Machine Learning FEA Engineer
Apple · Culver City ·
- Category
- ML ai
- Experience
- 3+ years
- Company size
- 1000+
Engineer on Apple's Product Design FEA team building machine learning methods grounded in predictive finite element simulations to assess design risks and optimize trade-offs for hardware like iPhone, iPad, Mac, Apple Watch, and Vision Pro. Core work spans Python, deep learning/optimization, and computational mechanics (FEA).
Imagine what you can do here! We are committed to pushing the boundaries of innovation and engineering excellence in product designs through machine learning and FEA simulations. We truly believe in the power of predictive simulation to make the impossible possible, transform industries and improve people’s lives. As a member of the Product Design FEA team, you will play a pivotal role in developing innovative machine learning technologies and directly impact the success of new iPhone, iPad, Mac, Apple Watch, Vision Pro and many more future products. Come join us and put a dent in the universe!
As a core member of the product design team, you will be responsible for developing and implementing ground-breaking machine learning methods that are based on predictive finite element simulations and important design load cases. The machine learning models will drive rapid design iterations by assessing potential risks and optimizing design trade-offs. You will be fully integrated with the product design team from the earliest stages to engineer ground breaking products.
Minimum Qualifications
- Strong Expertise in Machine Learning, Deep Learning, and Optimization
- Knowledges of Finite Element Analysis and/or other numerical methods in computational physics and mechanics
- Proficiency in Python and relevant packages for ML
- Outstanding communication skills
- Passion for creating innovative, high-quality products
- Desire to work in a fast-paced environment with passion for creating cutting edge products
- M.S. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline along with 3+ years of relevant experience
Preferred Qualifications
- Strong expertise in GNNs, CNNs, and transformer-based architectures
- Implement and optimize these models for large-scale datasets on scalable ML platforms
- Ability to work independently in white space and deal with an incredible fast-paced environment
- Excellent cross-functional collaboration and written and verbal communication skills
- Ph.D. in Computer Science, Machine Learning, Mechanical Engineering, or a similar discipline
- Publications in top journals or conferences