Software Engineer - Applied ML

Summary

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. We are looking for a seasoned engineer who can help define where this work goes next, not just how it gets built. 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.

Minimum Qualifications

Experience building and shipping production software, including technical leadership of multi-engineer projects. 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. Data processing: Skilled in working with large, noisy datasets. 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 Qualifications

Proven track record of setting technical strategy and roadmaps for ML-driven product features, and of influencing decisions across organizations without direct authority. 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. Experience mentoring and growing engineers, and building alignment among technical and non-technical stakeholders. 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 present strategy and technical trade-offs clearly to senior leadership. 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.

Description

As a seasoned engineer on the location technologies team, you will drive the direction for feature development and strategy. As part of this role, you'll join a team of software engineers and researchers focused on identifying behavioral patterns and you will help shape new and enhanced user experiences by collaborating closely with teams across sensing, Siri, and apps. You will partner with engineering, product, and research leadership to set a multi-year technical roadmap, translate ambiguous product goals into clear technical strategy, and make the architectural and investment trade-offs that follow. Excited to take ownership of complex, end-to-end problems? You’ll design, build, and evaluate production ML systems that infer a device’s patterns by inference on data 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. You will help identifing new opportunities for location intelligence across Apple’s products, and mentor engineers to raise the technical bar for the whole team.