Senior Software Engineer - Regulatory AI & Connected Data
Apple
Software Engineering, Data Science, Compliance / Regulatory
Cupertino, CA, USA
Posted on May 7, 2026
At Apple, the Product Analytics 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.
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.
- Design, build, and ship AI-powered software systems that improve team efficiency, delivering incrementally and iterating based on user feedback
- Apply secure engineering practices throughout: secrets management, data classification, access control, and audit logging appropriate for compliance-sensitive data
- Build and maintain robust data pipelines that connect corporate data sources, ensuring data quality, lineage, and accessibility
- Effectively use & improve leading agentic harnesses to build software with your principles, through the development of skills, agents and MCPs
- Integrate AI and large language models into production systems with appropriate evaluation, guardrails, and monitoring - treating models as components, not magic.
- Ensure that there is an audit trail for traceability/lineage for AI/LLM based decisions
- Establish and maintain continuous delivery pipelines, optimizing for the DORA metrics: deployment frequency, lead time, change failure rate, and mean time to recovery
- Build observability into every system from day one - instrumentation, structured logging, alerting, and dashboards that give the team confidence to ship fast
- Write clean, testable, well-factored code; practice continuous integration, continuous refactoring, and small batch delivery as daily habits
- Actively explore the PACE team’s domain, emerging tools, and adjacent problem spaces - bring new ideas and challenge assumptions
- Work directly with PACE team’s domain experts to understand problems deeply before building solutions
- Collaborate across teams and organizations to integrate data sources and align on technical direction
- Contribute to the engineering culture of a new team - shaping practices, running retrospectives, and helping the team continuously improve
- Represent your work through demos, design discussions, and clear written communication
- 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
- 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