Software Data Engineer, Sensing & Connectivity

• 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.
• 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.
• 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.

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.
• 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.
• 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.