ML Engineering Manager — Developer Tools & Platforms

Apple Inc.; Austin (TX); On-site; USD 243,000 - 364,000; Full time

Benefits offered by this job

Comprehensive medical and dental

Retirement benefits

Education reimbursement

Employee stock programs

Job summary

Apple Inc. in Austin seeks a Machine Learning Engineering Manager to lead development of ML tools and frameworks across the software development workflow.

You will guide data scientists and ML engineers to train, deploy, and optimize models that enhance our development tools and infrastructure. The role emphasizes cross-functional collaboration, data-driven decisions, and ownership of critical components that directly impact product quality and developer productivity.

Qualifications

• MS or PhD in Computer Science or related field.
• Experience building ML tools and frameworks.
• Strong ability to develop, debug, and support new technologies in a changing environment.
• Excellent written and verbal communication.

Responsibilities

• Spend most of your time working on data and machine learning systems.
• Collaborate with teammates to understand problems, define requirements, and implement solutions.
• Engage with other teams to identify issues and build long-term relationships that include knowledge sharing.
• Provide feedback loops to development teams backed by data and metrics.
• Develop ownership over components critical to Apple’s success.
• Contribute to engineering a high-performance system through algorithm design and optimization.

Skills

Data science background

Machine learning expertise

Collaboration

Communication skills

Education

MS or PhD in Computer Science

Tools

Python

TensorFlow

PyTorch

Job description

Apple Inc. in Austin seeks a Machine Learning Engineering Manager to lead development of ML tools and frameworks across the software development workflow.

You will guide data scientists and ML engineers to train, deploy, and optimize models that enhance our development tools and infrastructure. The role emphasizes cross-functional collaboration, data-driven decisions, and ownership of critical components that directly impact product quality and developer productivity.