AIML Quality Engineering Manager, Responsible AI and Safety

Apples Responsible AI and Safety team focuses on innovative technologies methodologies and research to enable fantastic user experiences and to push the frontier of machine learning. Our team is looking to hire a leader with a strong track record in Applied Research who is passionate about ML and foundation models with a focus on responsibility fairness and this role you will lead the research and application of ML methods for technologies that power breakthrough user experiences while upholding Apples values privacy and quality standards.

This role leads Apples Quality Platform Engineering function within Responsible AI the team responsible for giving engineering teams across Apple Intelligence fast trustworthy safety signal before code merges and for extending safety testing coverage to every hardware platform and form factor Apple ships. Most of this roles impact will come from two things: building a strong team and a healthy team culture from the ground up and establishing the flywheel that connects Quality Platform Engineering to Product Evaluations u0026 Research and Post-Ship Insights so fast signal coverage gaps and pipeline improvements consistently translate into real engineering decisions and clear leadership visibility rather than one-off dashboards nobody acts should be technically fluent comfortable with evaluation pipelines production ML and agentic systems and cross-platform testing enough to earn credibility with the team ask sharp questions and evaluate hard tradeoffs with good judgment. This role moves at a fast pace and you should be comfortable making product recommendations in ambiguous situations often with limited or imperfect data. Just as important are excellent communication strong product sense to prioritize a teams limited capacity against Apples highest-risk platforms and use cases and a track record of hiring and developing technical experience managing or building technical teams is required. Prior exposure to ML evaluation test infrastructure or safety-adjacent engineering work is strongly preferred.

Hire grow and build the culture of a team spanning test engineering applied ML and platform-specific expertisenOwn leadership reporting and safety sign-off representation for Quality Platform Engineering - translating pipeline health coverage and findings into risk and product terms for non-technical stakeholdersnSet and continuously reprioritize which feature clusters platforms and hardware form factors the team covers based on risk launch timing and known coverage gapsnDrive investment in test pipeline efficiency turnaround time infra cost and flakiness so pre-merge signal stays fast enough to be useful in the dev loopnExtend safety testing coverage beyond iPhone/iOS to every platform and device category Apple ships partnering with platform teams to close hardware-specific gapsnUpstream trusted datasets for product-critical safety cases so component teams have reliable ground truth to test against earlynChampion shift-left safety testing catching issues at the component level before they reach end-to-end evaluationnStay technically fluent enough to evaluate findings unblock the team on hard problems and track how model and pipeline behavior shifts across releasesnEnsure the teams signal stays consistent with Product Evaluations u0026 Researchs ship-time metrics so pre-merge results reliably predict end-to-end outcomes rather than drifting from themnManage occasional exposure to sensitive or policy-relevant content surfaced during testing and support team wellbeing around that exposure

5 years of technical team management or leadership experiencenExperience with ML evaluation production ML systems or test/CI infrastructure at scalenStrong engineering skills and experience writing production-quality code (Python or similar)nExperience working across multiple platforms or hardware form factors or a demonstrated ability to ramp quickly across unfamiliar platformsnExperience working with human-labeled or crowd-sourced evaluation data including reasoning about label noise and inter-rater agreement

Experience working on Responsible AI AI safety or trust u0026 safety-adjacent engineeringnExperience with generative model evaluation and common failure modesStrong organizational and operational skills working with large multi-functional diverse teamsnMS or PhD in Computer Science Machine Learning Statistics or related field or equivalent experience Familiarity with hardware/platform-specific testing considerations (e.g. on-device constraints new form factors)