Location Estimation Scientist, Sensing & Connectivity

Apple Apple · Big Tech · San Diego, CA · Software and Services

This role focuses on building and maintaining production-grade software systems and ML models for location intelligence using sensor data. It involves the full ML lifecycle from problem formulation to production deployment, with a strong emphasis on signal processing, data infrastructure, and scaling ML to hundreds of millions of devices.

What you'd actually do

  1. Design, build, and maintain high-quality, production-grade software systems — writing well-architected, testable, and performant code that others on your team rely on every day.
  2. Design and build signal processing pipelines and data infrastructure that ingest and interpret crowdsourced sensor data from hundreds of millions of devices — and apply ML to extract intelligence from those streams at scale.
  3. Own the full ML lifecycle: from problem formulation and feature engineering on raw sensor data, through model development and experimentation, to building the infrastructure that takes models reliably into production.
  4. Solve problems ranging from everyday navigation to life-critical emergencies, propose bold ideas, crunch data, leverage modern AI tools, and ship systems that run under demanding real-world efficiency requirements at global scale.

Skills

Required

  • signal processing theory
  • machine learning
  • model development
  • model training
  • model evaluation
  • production deployment
  • software engineering fundamentals
  • clean code
  • maintainable code
  • well-tested code

Nice to have

  • signal processing algorithms
  • sensor fusion algorithms
  • ML to sensor data challenges
  • classification
  • regression
  • anomaly detection
  • signal modeling
  • large-scale data pipelines
  • crowdsourced data
  • sensor data
  • telemetry data
  • Python
  • Java
  • ML development
  • algorithm development
  • infrastructure development
  • communication skills
  • complex systems explanation
  • algorithmic ideas explanation
  • curiosity
  • problem-solving

What the JD emphasized

  • production deployment
  • production environments
  • production systems
  • production environments
  • production environments

Other signals

  • ML models
  • production deployment
  • large-scale data pipelines
  • sensor data