Apple is where individual imaginations gather together, committing to the values that lead to great work. It's the diversity of our people and their thinking that inspires the innovation that runs through everything we do. Here, you'll do more than join something — you'll add something.
Artificial intelligence is one of the most profound technologies of our lifetime. The central AI/ML Data Operations team builds software solutions that enable global data collection for machine learning at scale.
We're seeking a hands-on engineering manager to lead a team building scalable data pipeline and fullstack application solutions. You will drive software development for a global data operations organization and be part of Apple's most strategic initiatives.
Description
As an engineering manager on the AI/ML Data Operations team, you will lead a team of software engineers.
You will partner with ML researchers, data scientists, and product teams across Apple to understand requirements and deliver scalable software solutions. Your team's work will directly impact the development and quality for models powering Apple Intelligence and other strategic AI features.
Minimum Qualifications
- B.S. in Computer Science or equivalent degree
- 3+ years of experience in a software engineering team management role
- 5+ years experience as a software engineer
- Proven track record of leading engineering teams to deliver user-centric, production software products across multiple releases
- Excellent interpersonal and communication skills, especially with non-engineering partners and executive stakeholders
Preferred Qualifications
- Passion for building team cultures that emphasize teamwork, engineering excellence, customer focus and innovation
- Strong programming background in Python, Java, Swift, or similar modern languages, especially for fullstack application development
- Experience in designing, building, and maintaining Unix-based, scalable distributed or cloud-based software solutions
- Experience with high-performance, scalable data pipelines, especially for preparing datasets for AI/ML model development
- Ability to weigh in on architectural decisions and align engineering execution with product and business needs
