Software Engineer, Applied ML

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.

Description

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. 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 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.

Minimum Qualifications

• Machine Learning algorithms: Strong grasp of supervised/unsupervised learning, regression, classification, clustering, and model evaluation techniques.
• Data processing: Skilled in working with large, noisy datasets.
• Experience with libraries like NumPy, pandas, scikit-learn, and PyTorch or TensorFlow.
• 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
• 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.

• Having worked as an ML practitioner in an industrial setting
• Laser focus on customer impact and product experience.
• Deep Learning: Experience with CNNs, RNNs/LSTMs, Transformers, etc. depending on the application domain.
• 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.