Software Development Engineer

Apple•Sunnyvale, CA

About The Position

Here is your opportunity to come and join an exciting engineering team responsible for building next-generation health sensors and features. You will be responsible for building new web-based monitoring and visualization platforms, extracting insights from sophisticated data sets, and developing intelligent automation to support multiple user studies per year ranging from focused studies with tens of participants to large-scale studies with thousands of participants. If you are passionate about health research and development, this is an exciting opportunity. At Apple, phenomenal ideas have a way of becoming phenomenal products, services, and customer experiences very quickly, and making the kind of impact that changes people's lives. We are seeking a software engineer to build backend systems, develop visualization tools, and help us improve the platforms used for research and development. The research studies we launched are helping Apple improve existing health experiences with our products, and build new ones. As a member of our fast-paced group, you will lead design reviews, mentor other engineers, and directly influence both the technical and product roadmap for the web tools that power our health user studies. Your work will directly impact the development of health-related features in Apple products. Data Integrity, in this role, means ensuring the collection of high-quality data from health user studies through robust data analysis pipelines and operator-facing tools and dashboards. You will work closely with cross-functional teams to identify user study problems and advise decision-making through data analysis. This role focuses on building new data and platform solutions to improve the usability of our systems for large-scale user studies that gather critical data to design and qualify Apple's health software products. You will design and build new web-based monitoring and visualization tools that give study teams real-time insight into study health and data quality, serving multiple concurrent studies that range in scale from tens to thousands of participants. You will leverage LLM-based agentic workflows not only within the product itself by integrating OpenAI/Anthropic APIs to create richer, smoother user experiences in study tools, but also within your own development lifecycle, using LLMs in a closed-loop workflow spanning planning, architecture, design review, implementation, PR generation, and deployment. A key part of this role is partnering directly with the researchers, engineers, and study operations teams who use your tools daily. Each study often comes with its own custom requirements, and you will deeply engage with stakeholders to understand their specific needs, gather feature requests, and prioritize improvements via established planning processes all while upholding Apple's privacy-first policies when handling sensitive health data. You are not just building — you are owning the relationship between your platform and the people who depend on it.

Requirements

Bachelor’s degree in Computer Science, Engineering, Informatics, or equivalent with relevant proven experience with biomedical, health, or sensitive data.
3+ years of software engineering experience.
Strong software development skills, with proficiency in Python.
Experience integrating LLM-based functionality (e.g., OpenAI, Anthropic APIs) into production web tools to create richer, smoother user experiences.
Experience leveraging LLMs in a closed-loop development workflow — from planning and architecture, to design review, to implementation, to generating pull requests, to deployment.
7+ years of software engineering experience, 3+ years in backend web service development.
Experience building web application backends for data monitoring and visualization (e.g., Flask, FastAPI, or similar frameworks; REST APIs; serving data to frontend dashboards).
Demonstrated skill and experience with distributed data processing systems such as PySpark and SparkSQL.
Demonstrated experience leading design reviews, mentoring engineers, and influencing technical/product roadmaps.
Experience with tools such as Git, Docker, Jupyter notebooks, Linux command line.
Demonstrated ability to engage directly with tool users and stakeholders to gather requirements, understand workflows, and translate needs into platform features and improvements.
Experience working with sensitive or regulated data under strict privacy requirements.

Nice To Haves

Familiarity working with systems such as blob storage, elastic compute, and virtual instances.
Experience with technologies such as Delta Lake, AWS.
Experience with container orchestration (Kubernetes or similar).
Experience with workflow orchestration (Apache Airflow or similar).
Experience with basic time series data analysis such as trend detection, anomaly identification, signal processing fundamentals.

Responsibilities

Build new web-based monitoring and visualization platforms.
Extract insights from sophisticated data sets.
Develop intelligent automation to support multiple user studies per year.
Build backend systems.
Develop visualization tools.
Improve platforms used for research and development.
Lead design reviews.
Mentor other engineers.
Directly influence both the technical and product roadmap for the web tools that power health user studies.
Ensure the collection of high-quality data from health user studies through robust data analysis pipelines and operator-facing tools and dashboards.
Work closely with cross-functional teams to identify user study problems and advise decision-making through data analysis.
Build new data and platform solutions to improve the usability of systems for large-scale user studies.
Design and build new web-based monitoring and visualization tools that give study teams real-time insight into study health and data quality.
Leverage LLM-based agentic workflows within the product by integrating OpenAI/Anthropic APIs.
Leverage LLMs in a closed-loop workflow spanning planning, architecture, design review, implementation, PR generation, and deployment.
Partner directly with researchers, engineers, and study operations teams.
Understand specific needs, gather feature requests, and prioritize improvements via established planning processes.
Uphold Apple's privacy-first policies when handling sensitive health data.
Own the relationship between your platform and the people who depend on it.