Job Description

Join the Sensing and Connectivity - Location Context team that powers critical experiences in Apple products. Our team works on providing personalized insights from users' daily patterns through multimodal sensing and AI models. This team combines advanced machine learning, including integration, adaptation, and tuning of foundation models, with contextual sensing, bolstered by innovative study design, to derive user insights for Maps and Fitness applications, Journal — among many more.

As the leader of this team, you will have the rare and rewarding opportunity to shape upcoming products that will delight and inspire our customers. You and your team will own the quality of cutting-edge location intelligence features powered by ML models across various operating
systems, including iOS and watchOS. We develop science-backed designs of AI/ML models that
capture the complex interplay of human health and behavior, providing valuable insights directly to our customers. We foster innovation and embrace new technology to enhance our creative capabilities. We are seeking a manager who pairs a working understanding of ML with a track
record of building and growing teams that validate these models and the supporting software.
If this is you, we would be delighted to hear from you.

The Sensing and Connectivity QE team is looking for a Software Development Manager in Test to lead a talented group of engineers who validate the algorithms and AI models behind our location context features. We work in a fast-paced environment that depends on a tight relationship between test and development. In this role you will:

- Own the quality assurance strategy for our ML models and for the end-to-end experiences powered by location context, including validation of training data, features, and labels.
- Set the technical direction for how the team validates these algorithms — challenging the design and implementation, turning assessments of AI model outputs into prioritized algorithm improvements, and holding the team to the same rigor in their own analysis and conclusions.
- Lead the team in developing the automated tests, analytics, and monitoring that qualify features, measure performance and system memory usage, and continuously track quality across releases.
- Identify the KPIs that define project success, build the dashboards behind them, and represent the team's results in user study, code, and data reviews as well as to engineering and executive audiences.
- Drive bug resolution across the broader software engineering organization and with partner teams across Apple, escalating and negotiating as needed to hold the quality bar.
- Work closely with development, program management, and cross-functional partners — from upstream hardware to downstream apps — to drive excellent end-to-end quality aligned with release schedules.
- Set a vision for the team's future direction, identify new opportunities, and build stakeholder buy-in to pursue them.
- Hire, coach, mentor, and grow the team — building on individual strengths and creating meaningful career growth.

Preferred Qualifications

Hands-on engineering background, including prior experience with software development sufficient to set technical direction and critically evaluate the team's work.
Experience leading a team that builds and maintains data pipelines for large-scale data processing.
Experience working at the intersection of hardware and software is a plus.
Ability to work in a dynamic environment with shifting priorities and schedules, with excellent analytical, problem-solving, and communication skills.
Passionate about location technologies and fitness

Minimum Qualifications

BS, MS, or PhD in Computer Science / Electrical Engineering / Mechanical Engineering or equivalent
5+ years of relevant industry experience
2+ years leading and managing an engineering or quality team, with a track record of growing engineers' skill sets and creating meaningful career growth.
Experience in validating AI models, crafting benchmarks and evaluation protocols, and performing statistical analysis.
Proficient in designing AI assisted workflows, using GenAI to automate and accelerate testing, debugging and metrics analysis.