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Software Test Engineer, Sensing & Connectivity
AppleCupertino, California, United States$150,400 - $277,600
Apply 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, Fitness, Journal - among many more. As a
member of our dynamic group, you will have the rare and rewarding opportunity to craft upcoming products that will delight and inspire our customers. Additionally, you will have the opportunity to work on 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 an engineer with a fundamental understanding of ML, the ability to validate these models, and the supporting software pieces. If this is you, we would
be delighted to hear from you.
The Sensing and Connectivity QE team is looking for a Software Development Engineer in Test to 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 of the end-to-end experiences powered by location context, and define the quality assurance strategy for our ML models, including validation of training data, features, and labels.
- Challenge the design and implementation of the algorithms, and back your conclusions with sound analysis.
- Develop automated tests that qualify features and measure performance and system memory usage, along with automated monitoring and quality metrics that continuously track feature performance.
- Identify the KPIs that define the project's success, build the dashboards behind them, and track progress across releases.
- Generate synthetic data at scale to validate on-device models across a large number of highly variable environments, applying the reasoning, analytics, and data-science judgment, and GenAI where it accelerates the work, needed to achieve meaningful variation and coverage.
- Build data-collection and replay infrastructure that scales across diverse real-world scenarios while adhering to Apple's privacy standards.
- Profile, process, and evaluate large structured and unstructured datasets.
- Participate in and present at user study reviews, code reviews, and data reviews.
- Communicate with upstream (e.g., hardware) and downstream (e.g., app) stakeholders to drive the end-to-end user experience of the feature.
- BS, MS, or PhD in Computer Science / Electrical Engineering / Mechanical Engineering or equivalent.
- 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.
- Proficient at coding in Python and/or MATLAB.
- Proficient at data analysis, data visualization, and reporting.
- Familiarity with building data-collection and replay tooling and evaluation pipelines for large-scale data processing.
- Experience working with databases (SQL/NoSQL), comfortable with query languages and designing tables, views, and indices.
- Familiarity testing hardware and/or testing software (including designing, implementing, and executing test plans) is a plus.
- Detail-oriented, able to find bugs, investigate and debug issues, and drive solutions to difficult problems.
- Excellent analytical and problem-solving skills, self-motivated, persistent, and solution-oriented.
- Passionate about location technologies and fitness.