Software Engineer, Frameworks/data, Sensing & Connectivity

Apple Apple · Big Tech · Cupertino, CA · Software and Services

Software Engineer focused on data collection, processing, and ML training data curation for on-device sensors and wireless technologies. The role involves building tools for data analysis, creating datasets for LLM benchmarking, and developing frameworks for evaluating generative AI output quality and reliability. It also includes designing data pipelines for sensor data and collaborating with teams to create intelligent experiences.

What you'd actually do

  1. Build apps and tools for collecting data from internal users, managing and processing data sets, analyzing data to derive insights, and creating visualizations to communicate these insights.
  2. Create tools to explore and interrogate data in depth, enabling teams to uncover patterns and validate hypotheses.
  3. Build and curate specialized datasets for benchmarking LLM accuracy and reliability, with focus on information and content integrity.
  4. Manage and curate training data for ML models, ensuring data quality, relevance, and ethical, privacy-preserving sourcing.
  5. Design and maintain scalable, privacy-preserving data pipelines that process anonymous aggregated sensor data from mobile devices to analyze real-world impact of features.

Skills

Required

  • Bachelors or Masters degree in a quantitative / technical field
  • Experience with large language models
  • Excellent communication and presentation skills
  • Programming skills in Python
  • Experience with data visualization techniques and software

Nice to have

  • Experience working with sensor data, IoT systems, or mobile device telemetry.
  • Programming skills on Apple’s platform - Swift, Objective-C, Foundation Framework
  • Experience with big data systems and tools like SQL, Hive, Spark.
  • Experience with geospatial data analysis and location-based services
  • Knowledge of climate science, environmental impact measurement, or sustainability metrics
  • Strong analytical and quantitative skills
  • Experience with MCP Servers, AI Agents, advanced Prompt Engineering, and using AI for complex automated data analysis
  • Proficiency working independently and proactively with stakeholders

What the JD emphasized

  • large language models
  • output evaluation and benchmarking
  • data privacy, security, and ethical data handling practices
  • AI Agents

Other signals

  • data collection
  • ML training data
  • LLM evaluation
  • data pipelines
  • generative AI quality