Machine Learning Engineer, Productivity Apps

At Apple, new ideas have a way of becoming phenomenal products, services, and customer experiences very quickly! The Productivity Apps team—the team behind apps like Notes, Freeform, and iWork— needs your help shaping the next generation of productivity tools by working on pioneering technologies to surprise and delight our users. You will be working alongside our world-class creatives, designers, scientists, and engineers to help innovate in the productivity space in ways that only Apple can. This is a highly visible, highly impactful opportunity!

As a Machine Learning Engineer focused on data, you'll be the expert on what's in our datasets and how data characteristics impact model performance. Your primary responsibility will be profiling and analyzing data to surface quality issues, identify gaps, and guide improvements to both evaluation and training datasets. Your deep understanding of our data will drive informed decisions across our ML pipeline and will be critical to our success in delivering high-quality features to our customers.

• Collaborating closely with your research colleagues to understand and document data requirements needed for successful model training.
• Sourcing, cleaning, and preprocessing data for our machine learning training pipelines.
• Developing hypotheses for dataset improvement through deep statistical analysis using off-the-shelf tools or tools you custom build for yourself.
• Designing and conducting iterative smaller-scale training experiments to validate your hypotheses.
• Contributing improvements to our training pipelines.

Key Qualifications

• 3+ years of experience contributing to machine learning models in production environments.
• Strong background in statistical analysis and modeling, including correlation analysis, clustering methods, probability theory, principal component analysis, outlier detection, and data visualization.
• Hands-on experience improving large training datasets consisting of both structured and unstructured data.
• Experience reading research papers and the ability to comprehend and build on key ideas.
• Strong programming skills and proficiency with numeric/statistical libraries like pandas, numpy, scipy, etc.
• Strong problem-solving and communication skills and the ability to communicate your ideas through effective data visualizations.

Education & Experience

• MS or PhD in Computer Science, Machine Learning, Statistics, or related field.

Additional Requirements

• Experience with distributed computing frameworks (e.g., Spark, Hadoop) for large-scale data processing.
• Experience with deep learning toolkits like PyTorch, JAX, TensorFlow, etc.
• Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and ML deployment tools.

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $181,100 and $318,400, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple’s discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple’s Employee Stock Purchase Plan. You’ll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses — including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits.

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant.