Software Data Engineer, Sensing & Connectivity

Summary

Imagine what you could do here! At Apple, new insights have a way of becoming extraordinary products, services, and customer experiences very quickly. We are looking for a Software Data

Engineer to build the tools, services, and data pipelines that support model evaluation at scale, across many models and high volumes of evaluation runs. You will join a team that takes a quality-first approach to build solutions that keep our systems reliable and accurate. The ideal candidate owns their work from prototype through production, is fluent with agentic coding tools, and keeps codebases lean through disciplined code review, branching, and CI/CD. If you want your evaluation work to shape how models ship, we'd like to hear from you.

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 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.

About the role

Our team builds frameworks, services, and tools that accelerate feature development for engineering teams. We bring a quality-first perspective to end-to-end solutions: dependable systems that our engineering teams can trust day after day.

In this role, you'll apply your quality engineering expertise to the evaluation workflows that measure the reliability and accuracy of generative AI systems. You'll integrate our evaluation platform with enterprise services, build the data pipelines that capture and store evaluation results, and establish validation frameworks that maintain Apple's standards for quality.

You bring

• A commitment to lean codebases: small, focused, well-tested changes, reuse of existing components over new ones, and deletion of what is no longer needed.
• Strong source code management practices, including code review (giving and receiving) and disciplined branching.
• CI/CD experience: automated testing, reproducible builds, and safe rollouts.
• Data engineering experience building and operating pipelines and data stores.

Responsibilities

• Develop and support new and existing features in a hands-on capacity.
• Build tools and services that support model evaluation at scale.
• Build and maintain pipelines for evaluation datasets, run results, and metrics.
• Design data models and storage that make evaluation results reproducible and queryable.
• Design and maintain validation frameworks that measure the reliability and accuracy of synthetic data.
• Integrate the evaluation platform with enterprise services.
• Partner with cross-functional teams to coordinate the complex interdependencies of application development.
• Deploy, monitor, and operate services on production-grade infrastructure.

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or a related field, or 3 years of equivalent work experience.
• 3+ years of experience building and operating production software in Python.
• Experience debugging distributed systems or data pipelines in production, using logs, metrics, and task state to find root causes.
• Experience designing input validation and configuration contracts for systems where late failures are costly.
• Proven, hands-on use of agentic AI coding tools in day-to-day software development, including judging where they are effective and where their output needs human verification.
• Strong communication skills.

Preferred Qualifications

• Experience with model evaluation frameworks or ML infrastructure.
• Experience working directly with partner teams or internal customers to define and ship features.
• Experience measuring the domain gap between synthetic and real data, including visual statistics and non-visual properties such as metadata and label distributions.
• Experience with generative image, video, or large language model APIs, including structured output.
• Familiarity with image and video file formats and metadata standards.
• Experience with batch compute platforms or job schedulers.