Machine Learning Engineer
Job Description
Apple's machine learning team in Sunnyvale, CA is seeking a Machine Learning Engineer to advance computer vision and graphics features for Face and Body/vision technologies. This on-site role covers the full ML lifecycle, from data collection and model experiments to validation in real-world contexts, delivering capabilities for iOS and VisionOS while collaborating with ML, data, and software engineers.
Responsibilities
- Design data collection strategies and manage data processing as part of the ML cycle.
- Design and run model experiments within the ML lifecycle.
- Conduct validation and QA in real-world settings.
- Advance applied research to adapt state-of-the-art methods or implement new approaches to ship features on iOS and VisionOS platforms.
- Collaborate with other ML engineers, data engineers, and software engineers internally and cross-functionally.
Requirements
- 3+ years of experience delivering ML projects for computer vision or graphics applications.
- Software engineering skills with proficiency in Python.
- Experience with PyTorch.
- BA/BS degree in computer vision, computer graphics, machine learning or related field.
Technologies
- Python
- PyTorch
Benefits
- Comprehensive medical and dental coverage
- Retirement benefits
- A range of discounted products and free services
- Reimbursement for certain educational expenses, including tuition
- Apple discretionary employee stock programs
- Eligible for discretionary restricted stock unit awards
- Ability to purchase Apple stock at a discount via Employee Stock Purchase Plan
- Discretionary bonuses or commission payments
- Relocation
Pay & Benefits
Base pay range for this role is USD 150,400 to 277,600 per year, with the final amount determined by skills, qualifications, experience, and location.
Preferred Qualifications
- MS or PhD in computer vision, computer graphics, machine learning, computer science, computer engineering or related fields.
- Self-motivated with a proven ability to prioritize and deliver tasks on schedule.
- Excellent communication and experience working with cross-functional teams.