Software Data Engineer, Sensing & Connectivity

Apple • Cupertino, CA

5d

About The Position

Apple is seeking a Software Data Engineer to build tools, services, and data pipelines that support model evaluation at scale across many models and high volumes of evaluation runs. The role is part of a team that builds frameworks, services, and tools to accelerate feature development for engineering teams, with a quality-first focus on dependable systems.

The engineer will apply quality engineering expertise to workflows that measure the reliability and accuracy of generative AI systems. This includes integrating an evaluation platform with enterprise services; building pipelines for evaluation datasets, run results, and metrics; and creating data models, storage, and validation frameworks that make results reproducible, queryable, and reliable. The role also involves developing and supporting features hands-on, coordinating with cross-functional teams, and deploying, monitoring, and operating services on production-grade infrastructure.

The ideal candidate owns work from prototype through production, uses agentic coding tools in day-to-day development, and maintains lean codebases through focused, well-tested changes, code review, disciplined branching, and CI/CD.

Requirements

• Commitment to lean codebases, including small, focused, well-tested changes, reuse of existing components, and removal of unneeded code.
• Strong source code management practices, including giving and receiving code review and disciplined branching.
• CI/CD experience, including 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.
• At least 3 years of experience building and operating production software in Python.
• Experience debugging distributed systems or production data pipelines using logs, metrics, and task state to identify root causes.
• Experience designing input validation and configuration contracts for systems where late failures are costly.
• Hands-on use of agentic AI coding tools in day-to-day software development, including judging their effectiveness and when human verification is needed.
• Strong communication skills.

Nice To Haves

• 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.

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 interdependencies in application development.
• Deploy, monitor, and operate services on production-grade infrastructure.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level