Machine Learning Engineer - Data, Productivity Apps
Apple
Machine Learning Engineer - Data, Productivity Apps
Job Description
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.
Locations
- Cupertino, California, United States 95014
Salary
Estimated Salary Rangemedium confidence
25,000,000 - 50,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- data profilingintermediate
- data analysisintermediate
- data quality assessmentintermediate
- dataset evaluationintermediate
- dataset improvementintermediate
- machine learning pipeline managementintermediate
Required Qualifications
- MS or PhD in Computer Science, Machine Learning, Statistics, or related field. (degree in phd in computer science)
- 3+ years of experience contributing to machine learning models in production environments. (experience, 3 years)
- Strong background in statistical analysis and modeling, including correlation analysis, clustering methods, probability theory, principal component analysis, outlier detection, and data visualization. (experience)
- Hands-on experience improving large training datasets consisting of both structured and unstructured data. (experience)
- Experience reading research papers and the ability to comprehend and build on key ideas. (experience)
- Strong programming skills and proficiency with numeric/statistical libraries like pandas, numpy, scipy, etc. (experience)
- Strong problem-solving and communication skills and the ability to communicate your ideas through effective data visualizations. (experience)
Preferred Qualifications
- Experience with distributed computing frameworks (e.g., Spark, Hadoop) for large-scale data processing. (experience)
- Experience with deep learning toolkits like PyTorch, JAX, TensorFlow, etc. (experience)
- Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and ML deployment tools. (experience)
Responsibilities
- 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.
Tags & Categories
Answer 10 quick questions to check your fit for Machine Learning Engineer - Data, Productivity Apps @ Apple.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs
Machine Learning Engineer - Data, Productivity Apps
Apple
Machine Learning Engineer - Data, Productivity Apps
Job Description
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.
Locations
- Cupertino, California, United States 95014
Salary
Estimated Salary Rangemedium confidence
25,000,000 - 50,000,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- data profilingintermediate
- data analysisintermediate
- data quality assessmentintermediate
- dataset evaluationintermediate
- dataset improvementintermediate
- machine learning pipeline managementintermediate
Required Qualifications
- MS or PhD in Computer Science, Machine Learning, Statistics, or related field. (degree in phd in computer science)
- 3+ years of experience contributing to machine learning models in production environments. (experience, 3 years)
- Strong background in statistical analysis and modeling, including correlation analysis, clustering methods, probability theory, principal component analysis, outlier detection, and data visualization. (experience)
- Hands-on experience improving large training datasets consisting of both structured and unstructured data. (experience)
- Experience reading research papers and the ability to comprehend and build on key ideas. (experience)
- Strong programming skills and proficiency with numeric/statistical libraries like pandas, numpy, scipy, etc. (experience)
- Strong problem-solving and communication skills and the ability to communicate your ideas through effective data visualizations. (experience)
Preferred Qualifications
- Experience with distributed computing frameworks (e.g., Spark, Hadoop) for large-scale data processing. (experience)
- Experience with deep learning toolkits like PyTorch, JAX, TensorFlow, etc. (experience)
- Familiarity with cloud platforms (e.g., AWS, GCP, Azure) and ML deployment tools. (experience)
Responsibilities
- 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.
Tags & Categories
Answer 10 quick questions to check your fit for Machine Learning Engineer - Data, Productivity Apps @ Apple.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs