Machine Learning Engineering Manager - Evaluation, Responsible AI and Safety

Summary

Join Us in Shaping the Future of Generative AI at Apple! Are you passionate about building and leading teams that make AI systems safer, more inclusive, and globally representative? Apple is seeking an experienced Engineering Manager to lead a team shaping the future of responsible AI evaluation for the next generation of generative features. In this role, you will lead a team of machine learning engineers and data scientists working to assess, quantify, and improve the safety and inclusivity of Apple’s Generative-AI powered features and products. You and your team's work will tackle innovative problems in machine learning, particularly focused on large language models for text generation, diffusion models for image generation, and mixed model systems for multimodal applications. You will directly influence how we evaluate, align, and monitor the safety of large language and multimodal models, while upholding Apple's values, privacy, and quality standards. As part of Apple’s Responsible AI group within the Software Engineering organization, you’ll collaborate with cross-functional partners to minimize unintended consequences across people, systems, and society while elevating feature capabilities and the overall user experience. You’ll set technical direction, anticipate safety risks, and develop a team to deliver trusted, high‑quality AI experiences to users around the globe.

Minimum Qualifications

MS or PhD in Computer Science, Linguistics, Cognitive Science, HCI, Psychology, Mathematics, Physics, or a similar science or technology field with a strong basis in scientific data collection and analysis + at least 4 years of relevant work experience, or BA/BS with 8+ years of relevant work experience, with 2+ years directly managing engineering teams Experience gathering and analyzing varied language, image, voice, video, and/or multi-modal data, including human- and LLM-generated data Experience with LLM-Judges as well as human-in-the-loop techniques, including designing human annotation projects, writing guidelines, and dealing with highly multi-labeled, nuanced, and often conflicting data Proficiency in data science, machine learning, analytics, programming with Python & Pandas, and AI-assisted coding tools; experience with one or more plotting & visualization libraries Cross-functional collaboration skills to prioritize competing projects needs, set and maintain schedules for project milestones, and communicate with all levels of team members, as well as internal and external stakeholders A rigorous approach to metric development; interpretation of experiments and evaluations; and presentation to executives Comfortable leading a team that works with sensitive or potentially offensive content.

Preferred Qualifications

Experience working in the Responsible AI space Passion for building innovative, high‑impact products and teams that draw upon interdisciplinary skills Curiosity about fairness and bias in generative AI systems, and a strong desire to help make the technology more equitable A Social Science or Humanities background, or other demonstrable interest in socio-technical issues Prior scientific research and publication experience Proven track record of contributing to diverse teams in a collaborative environment Experience working with multimodal models, agentic systems, and/or diverse languages and locales Experience with compliance and regulatory needs in the AI space, specifically regarding child safety, clinical/health applications, or companionship chatbots Experience conducting large scale benchmarking across models and capabilities

Description

We are seeking a visionary Machine Learning Engineering Leader to build, mentor, and develop a high-performing team, driving Responsible AI Evaluation efforts for global generative AI products. In this role, you will provide critical technical direction for operationalizing policy and risk assessments into measurable criteria; designing datasets; developing LLM- and human-in-the-loop methods for data analysis; and overseeing the implementation of data pipelines to produce reliable, scalable insights to inform ship/no-ship product decisions. Pre and Post-launch, you will remain hands-on—analyzing evaluation results, conducting failure investigations, and leveraging human and automated feedback to continuously improve feature safety and build user trust. As a champion for your team, you will foster an inclusive culture of technical excellence, empower senior leadership with safety recommendations, and systematically remove roadblocks to ensure team success. This role heavily draws on applied data science, scientific investigation and interpretation, strategic cross-functional stakeholder management, including negotiating requirements and defending safety metrics to leadership, and metrics reporting and presentation to stakeholders & decision-makers.

Responsibilities

Develop metrics for evaluation of safety and fairness risks inherent to generative models and Gen-AI features Design datasets, identify data needs, and work on creative solutions, scaling and expanding data coverage through human and synthetic generation methods Develop sampling strategies and combine human annotation with auto-grading to deliver high-quality, high-confidence insights at a fast pace and large scale Use and implement data pipelines, and collaborate cross-functionally to execute end-to-end safety evaluations Drive operational excellence by automating manual workflows, optimizing evaluation velocity, and scaling infrastructure to meet rapid product release cycles. Distill project findings into insights for product engineering teams and safety policy development Work with highly-sensitive content with exposure to offensive and controversial content