Senior 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 Apples 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. nnPACE 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 feedbacknApply secure engineering practices throughout: secrets management data classification access control and audit logging appropriate for compliance-sensitive datanBuild and maintain robust data pipelines that connect corporate data sources ensuring data quality lineage and accessibilitynEffectively use u0026 improve leading agentic harnesses to build software with your principles through the development of skills agents and MCPs nIntegrate AI and large language models into production systems with appropriate evaluation guardrails and monitoring - treating models as components not that there is an audit trail for traceability/lineage for AI/LLM based decisionsnEstablish and maintain continuous delivery pipelines optimizing for the DORA metrics: deployment frequency lead time change failure rate and mean time to recoverynBuild observability into every system from day one - instrumentation structured logging alerting and dashboards that give the team confidence to ship fastnWrite clean testable well-factored code; practice continuous integration continuous refactoring and small batch delivery as daily habitsnActively explore the PACE teams domain emerging tools and adjacent problem spaces - bring new ideas and challenge assumptionsnWork directly with PACE teams domain experts to understand problems deeply before building solutionsnCollaborate across teams and organizations to integrate data sources and align on technical directionnContribute to the engineering culture of a new team - shaping practices running retrospectives and helping the team continuously improvenRepresent your work through demos design discussions and clear written communication

Bachelors Degree in Computer Science Computer Engineering related field or equivalent work experiencen7 years experience building and shipping production software systemsnStrong track record of delivering AI-powered systems at scale including model integration evaluation and production monitoringnDeep practical experience with modern software engineering practices: continuous integration continuous delivery trunk-based development and incremental deliverynProficient in Python and at least one other high-level programming languagenExperience building data pipelines and working with connected data across multiple sourcesnExperience with cloud infrastructure and container technologies including Kubernetes and DockernDemonstrated ability to build observability into production systems - metrics tracing logging and alertingnA curious mindset - you dig into unfamiliar domains ask why things work the way they do and seek out knowledge beyond your immediate responsibilitiesnExcellent written and verbal communication skills with both technical and non-technical audiences

Masters degree in Computer Science Computer Engineering related field or equivalent work experiencenExperience working in or building software for regulated industries (compliance legal safety or similar domain)nFamiliarity with the principles in Accelerate and practical experience improving DORA metrics in a team settingnExperience with test-driven development continuous refactoring small batch delivery and collective code ownershipnExperience securing AI/LLM systems that process sensitive or regulated data including prompt injection defense data handling policies and audit trail requirementsnExperience with LLM application patterns: retrieval-augmented generation prompt engineering evaluation frameworks and human-in-the-loop workflowsnExperience with MLOps practices including model versioning experiment tracking and performance monitoring in productionnTrack record of building systems that connect and make sense of heterogeneous data sources at enterprise scalenExperience helping establish engineering culture on a new or transforming team