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GenAI Research Engineer
AppleSeattle, Washington, United States$171,600 - $302,20010+ years
Apply 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.
- Develop and improve generative AI systems for health and wellbeing applications across the full feature development lifecycle.
- Independently design, run, and analyze ML experiments to deliver measurable quality improvements.
- Design and implement evaluation frameworks—including automated assessment tools, LLM-based autograders, and benchmarks—with rigorous validation of their reliability and validity.
- Apply statistical and interpretability methods to analyze evaluation data and model behavior, identify failure modes through adversarial testing and failure analysis, and drive actionable improvements.
- Build and maintain scalable, reusable pipelines for inference, training, and evaluation, integrated with large-scale data workflows.
- Work cross-functionally with engineers, clinical experts, designers, and hardware and software teams to bring features into production with real-world applicability and impact.
- BS and a minimum of 10 years relevant industry experience
- Proficiency in Python and ability to write clean, performant code and collaborate using standard software development practices.
- Technical expertise and hands-on experience crafting and evaluating machine learning solutions for user-facing applications.
- Experience 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 or PhD in a detailed quantitative field such as computer science, mathematics, statistics, economics, health informatics, or similar and 5 years relevant industry experience
- Strong communication skills, comfort working with multiple engineering teams on complex projects, and experience contributing to an inclusive team culture.
- Technical expertise in generative AI domains, such as large language model architectures, memory representation, planning, knowledge retrieval, natural language understanding.
- Hands-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.
- Experience with LLM-based evaluation systems and rigorous, evidence-based approaches to test development, e.g. quantitative and qualitative test design, reliability and validity analysis.
- Customer-focused mindset with experience or strong interest in building consumer digital health and wellness products.
- Knowledge of health informatics or experience with complex health data sources (e.g. EHRs, medical ontologies, wearables)
- Experience with building and deploying performant and scalable systems (full-stack)
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