Sequoia AIML - Machine Learning Engineer, Evaluation - Responsible AI and Safety
Summary
ML engineer on Apple's HCMI/Responsible AI team in Cambridge, assessing and improving the safety and fairness of Apple Intelligence's generative AI features. Day-to-day involves designing human annotation datasets and safety metrics, building data pipelines, and reporting evaluation findings, using Python and Pandas.
Join Us in Shaping the Future of Generative AI at Apple!
Are you passionate about making AI systems safer, more inclusive, and globally representative? Apple is seeking an experienced Senior Machine Learning Engineer to help shape the future of responsible AI evaluation for the next generation of generative features.
In this role, you will work to assess, quantify, and improve the safety and inclusivity of Apple’s Generative-AI powered features and products. Your 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 anticipate safety risks and deliver trusted, high‑quality AI experiences to users around the globe.
We are seeking a visionary Senior Machine Learning Engineer to drive Responsible AI Evaluation efforts for global generative AI products. In this role, you will provide critical technical execution for operationalizing policy and risk assessments into measurable criteria; designing datasets; developing LLM- and human-in-the-loop methods for data analysis; and implementing robust data pipelines to produce reliable, scalable insights that inform ship/no-ship product decisions.
Pre- and post-launch, you will be deeply 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 senior technical leader on the team, you will champion technical excellence, empower leadership with data-driven safety recommendations, and systematically solve complex engineering roadblocks. This role heavily draws on applied data science, scientific investigation, cross-functional collaboration, and defending safety metrics to stakeholders and decision-makers.
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
- 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 working with sensitive or potentially offensive content
Preferred Qualifications
- Experience working in the Responsible AI space
- A passion for building outstanding and innovative products. This position involves a wide variety of 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