Machine Learning Engineer

2 weeks ago


Cupertino, California, United States Apple Full time $120,000 - $200,000 per year
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

Description
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.

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

Preferred Qualifications
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.

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