Software Development Engineer - Location Technologies, Sensing & Connectivity

Job description

Our mission is to personalize the user experience on Apple devices based on where you go, when, and what those places mean to you. You're experiencing our work whenever you see a suggested location in Maps or Calendar, or browse your Memories in Photos or Journal. We're working for you whenever your phone engages Do Not Disturb While Driving or remembers where you parked. We're the Location Context team, and we build the location intelligence backbone powering Maps Visited Places, Siri location suggestions, and predictive features across the OS. We're looking for engineers who love solving hard problems at the intersection of location state estimation, on-device machine learning, and privacy-preserving systems. Are you excited by any of these challenges? • Building location state estimators that fuse GPS, WiFi, IMU, and altimeter data to understand not just where users are, but what floor of a building they're on • Designing ML models to infer the semantics of a place and forecast where the device will go next, entirely on-device with strict power and memory budgets • Developing clustering algorithms and data pipelines that process billions of location events while preserving user privacy • Optimizing system performance at massive scale—where a 1% edge case impacts 10 million devices and a power regression of 0.1% matters • Collaborating with Maps, Siri, Photos, HomeKit, Journal, and Safety teams to power features that require deep contextual understanding If this sounds like you, read on. Minimum Qualifications: 5+ years experience developing commercial software, preferably systems-level or embedded software running on resource-constrained devices Strong programming skills in C, C++, Objective-C, or Swift, with solid foundation in algorithms, data structures, and computational complexity Working knowledge of statistics and probability, including comfort with histograms, probability distributions, Bayesian inference, and hypothesis testing Experience evaluating and optimizing system performance: memory footprint, CPU usage, power consumption, and I/O Preferred Qualifications: Deep expertise in location technologies: GPS/GNSS positioning, WiFi-based localization, indoor positioning, sensor fusion for state estimation, or IMU-based dead reckoning. If you've built location estimators that fuse multiple sensor modalities, we especially want to hear from you. Experience with machine learning for time-series data, spatial data, or behavioral prediction. On-device ML experience (model size optimization, quantization, power-efficient inference) is a strong plus. Background in signal processing, Kalman filtering, particle filters, or other probabilistic state estimation techniques. Experience with clustering algorithms (DBSCAN, hierarchical clustering, etc.) and unsupervised learning applied to spatial or temporal data. Track record of shipping production systems that operate at scale under resource constraints (mobile, embedded, or edge computing environments). Strong collaboration skills and ability to work effectively across teams with diverse expertise. At Apple, you'll partner closely with teams in sensing, connectivity, privacy, and application frameworks. You'll need to communicate clearly, plan collaboratively, and execute flexibly. Experience with performance profiling tools (Instruments, dtrace, etc.) and systematic optimization of CPU, memory, and power usage. Experience with large-scale data analysis for offline algorithm development, model validation, and performance evaluation across diverse user populations.