About the job
Our mission is to personalize the Apple user experience based on where you go, when you’re there, and what those places mean to you. We’re developing intelligent systems that understand location context and help users achieve what they want— wherever they are. You’ve seen our work in action through suggested locations in Maps, Journaling Suggestions for outings and trips, and curated Memories in Photos. We’re seeking motivated, experienced engineers to elevate our software’s intelligence, performance, and impact. Do you have experience linking users and devices to points of interest on a map? Are you an ML practitioner energized by the challenge of delivering rich contextual intelligence within a tight resource budget? If so, we’d love to hear from you.
Responsibilities
You’ll design, build, and evaluate production ML systems that infer a device’s patterns by inference on date like GPS, Wi-Fi, and accelerometers and higher semantic signals — combining estimation techniques with machine learning. You’ll test and refine your work, use it yourself, track metrics, and iterate for quality.
Qualifications
Minimum
Proven experience taking machine learning models through the entire lifecycle—from ideation, data collection, and prototyping to production deployment and monitoring
Demonstrated experience working with time-series analysis, sequential modeling, or spatial-temporal datasets.
Experience handling sparse, irregular, or highly imbalanced data streams typical of real-world sensor or location data
Experience shipping production software for mobile and/or other resource-constrained devices. Tight memory, CPU, and schedule constraints motivate you and ignite your creativity. Capability of creating, analyzing, and modifying SW functionality, ideally in C++/Obj-C/Swift codebases
Experience with libraries like NumPy, pandas, scikit-learn, and PyTorch or TensorFlow.
Hands-on experience with applied probability, statistics, and empirical and/or ML algorithms. Classical estimation, signal processing, and/or training supervised ML models are relevant.
Bachelor’s or graduate degree in Computer Science, Computer Engineering, Mathematics, or a related field.
Preferred
Machine Learning algorithms: Strong grasp of supervised/unsupervised learning, regression, classification, clustering, and model evaluation techniques.
Having worked as an ML practitioner in an industrial setting
Laser focus on customer impact and product experience.
Some professional background in location and/or other wireless sensing technologies, including for example, GPS/GNSS, WiFi, indoor localization, and/or discrete localization.
Excellent communication, verbally and in writing. You can succeed in a collaborative environment, and are comfortable with what will sometimes feel like a high degree of uncertainty.
You can innovate within tight memory, CPU, and schedule constraints, and deliver on time. These constraints motivate you, and ignite your creativity.