Software Engineer - Regulatory AI & Connected Data

At Apple, the Product Analysis and Compliance Engineering (PACE) organization ensures that every product we ship meets the highest standards of regulatory compliance, product safety, and analytical rigor. We operate at the intersection of engineering, compliance, and data, delivering the insights, testing, and certification workflows that Apple's product programs depend on. Our teams navigate complex regulatory landscapes across dozens of global markets, managing a volume and velocity of compliance work that grows with every product Apple ships.

PACE is building intelligent systems at the intersection of AI, connected data, and compliance, making the organization dramatically more efficient. Our work connects disparate data sources, applies AI to extract insight and automate decision-making, and puts powerful tools directly in the hands of compliance engineers and analysts. We are seeking a Software Engineer who believes the best way to build great software is to ship early, measure relentlessly, and iterate based on real feedback and real data.

Description

As a Senior Software Engineer on this team, you will design, build, and ship software systems that apply AI to to improve the efficiency of the PACE team. You will work in small iterations, delivering working software early and often, and use data to guide what to build next. You believe that quality is built in - not bolted on - and that fast delivery and high standards reinforce each other. You will help establish the engineering culture of a new team: lean practices, continuous delivery, production observability, and a relentless focus on outcomes over output. You are deeply curious - about about emerging AI capabilities, how users actually work, and how to make tools to enable success - and you channel that curiosity into building things that matter. You will collaborate closely with PACE domain experts to deeply understand their problems, and with data and AI practitioners to build systems that genuinely work at scale.

Minimum Qualifications

Bachelor's Degree in Computer Science, Computer Engineering, related field, or equivalent work experience

7+ years experience building and shipping production software systems

Strong track record of delivering AI-powered systems at scale, including model integration, evaluation, and production monitoring

Deep practical experience with modern software engineering practices: continuous integration, continuous delivery, trunk-based development, and incremental delivery

Proficient in Python and at least one other high-level programming language

Experience building data pipelines and working with connected data across multiple sources

Experience with cloud infrastructure and container technologies including Kubernetes and Docker

Demonstrated ability to build observability into production systems - metrics, tracing, logging, and alerting

A curious mindset - you dig into unfamiliar domains, ask why things work the way they do, and seek out knowledge beyond your immediate responsibilities

Excellent written and verbal communication skills with both technical and non-technical audiences

Preferred Qualifications

Master's degree in Computer Science, Computer Engineering, related field, or equivalent work experience

Experience working in or building software for regulated industries (compliance, legal, safety, or similar domain)

Familiarity with the principles in Accelerate and practical experience improving DORA metrics in a team setting

Experience with test-driven development, continuous refactoring, small batch delivery, and collective code ownership

Experience securing AI/LLM systems that process sensitive or regulated data, including prompt injection defense, data handling policies, and audit trail requirements

Experience with LLM application patterns: retrieval-augmented generation, prompt engineering, evaluation frameworks, and human-in-the-loop workflows

Experience with MLOps practices including model versioning, experiment tracking, and performance monitoring in production

Track record of building systems that connect and make sense of heterogeneous data sources at enterprise scale

Experience helping establish engineering culture on a new or transforming team