AIML - Sr Engineering Program Manager, Data
Apply NowCompany: Apple
Location: Cupertino, CA 95014
Description:
Summary
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something - you'll add something.
Do you love taking on challenges that create a positive impact? Are you passionate about empowering many ground-breaking intelligent experiences to be made? We're looking for people like you! As part of Apple's AI and Machine Learning org, you will be developing new ML-powered sensing and intelligence capabilities for home, health, and more. This role will drive the collection of high-quality training data from consumer devices for a variety of ground-breaking use cases.
Description
You will program manage Research and Applied ML's data collections:
- Working with modeling teams to determine data collection requirements
- Interfacing with our data operations teams to ensure completion and quality of collection and annotation
You will collaborate with teams and be the expert for many aspects of our data collections:
- Data collection methodologies
- Landscape of available data collection tools and infrastructure
- Compliance with Apple's legal, user consent, privacy and security policies as well as securing approvals
- Data science to ensure sufficient representative data is collected
- Interpreting evaluations and doing failure analysis on the collected data
You will also be responsible for scaling and optimizing our data collection efforts as a whole:
- Driving the roadmap for shared tooling and infrastructure for data collections
- Finding opportunities across modeling teams to share data and/or data collection efforts
Apple is where individual imaginations gather together, committing to the values that lead to great work. Every new product we build, service we create, or Apple Store experience we deliver is the result of us making each other's ideas stronger. That happens because every one of us shares a belief that we can make something wonderful and share it with the world, changing lives for the better. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. When we bring everybody in, we can do the best work of our lives. Here, you'll do more than join something - you'll add something.
Do you love taking on challenges that create a positive impact? Are you passionate about empowering many ground-breaking intelligent experiences to be made? We're looking for people like you! As part of Apple's AI and Machine Learning org, you will be developing new ML-powered sensing and intelligence capabilities for home, health, and more. This role will drive the collection of high-quality training data from consumer devices for a variety of ground-breaking use cases.
Description
You will program manage Research and Applied ML's data collections:
- Working with modeling teams to determine data collection requirements
- Interfacing with our data operations teams to ensure completion and quality of collection and annotation
You will collaborate with teams and be the expert for many aspects of our data collections:
- Data collection methodologies
- Landscape of available data collection tools and infrastructure
- Compliance with Apple's legal, user consent, privacy and security policies as well as securing approvals
- Data science to ensure sufficient representative data is collected
- Interpreting evaluations and doing failure analysis on the collected data
You will also be responsible for scaling and optimizing our data collection efforts as a whole:
- Driving the roadmap for shared tooling and infrastructure for data collections
- Finding opportunities across modeling teams to share data and/or data collection efforts