GenAI Research Engineer

The Health Personalization team builds outstanding technologies to support AI-driven health experiences that provide our users with understandable, actionable information about their health and wellbeing, and support them to achieve their health and wellness goals. As part of the larger Sensor Software & Prototyping team, we take a multimodal approach using a variety of sensors and user data signals across hardware platforms, such as camera, wearables, and natural language user input. We are committed to building deeply personal features that understand, anticipate, and adapt to users' behaviors and uphold Apple's deep commitment to privacy as a fundamental human right. Our team values expertise, innovation, and inclusivity. Come join us!

In this role, you will develop, evaluate, and continuously improve Generative AI systems for real-world health and wellbeing applications. You'll apply deep expertise in health-focused machine learning alongside rigorous evaluation methodology. Your work will directly shape customer-facing health products by turning evaluation results into scalable pipelines that drive data-informed model improvements across the full ML lifecycle. You'll collaborate with a fast-growing team of top scientists and engineers to build generative systems that meet the highest standards of quality, reliability, and alignment with human intent.

BS and a minimum of 3 years relevant industry experience\nProficiency in Python and ability to write clean, performant code and collaborate using standard software development practices.\nTechnical expertise and hands-on experience crafting and evaluating machine learning solutions for user-facing applications.\nExperience applying a scientific approach to drive machine learning innovation: developing hypotheses, designing experimentation strategies, and guiding data generation / collection / evaluation (e.g. user studies, annotation workflows, A/B tests).

MS and a minimum of 3 years of relevant industry experience or PhD in relevant fields.\nStrong communication skills, comfort working with multiple engineering teams on complex projects, and experience contributing to an inclusive team culture.\nTechnical expertise in generative AI domains, such as large language model architectures, memory representation, planning, knowledge retrieval, natural language understanding.\nHands-on experience developing complex generative AI systems in an applied setting, e.g. experience with post-training techniques like supervised fine-tuning, adapter training, and reinforcement learning from human feedback.\nExperience with LLM-based evaluation systems and rigorous, evidence-based approaches to test development, e.g. quantitative and qualitative test design, reliability and validity analysis.\nCustomer-focused mindset with experience or strong interest in building consumer digital health and wellness products.\nKnowledge of health informatics or experience with complex health data sources (e.g. EHRs, medical ontologies, wearables)\nExperience with building and deploying performant and scalable systems (full-stack)