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Description
Design and deploy AI-native tooling and agentic workflows to accelerate algorithm development and validation across motion sensing domains Scale algorithm validation to millions of sensor sessions using AI-orchestrated replay, with automated dataset selection, evaluation scheduling, and regression detection Build agentic data pipelines to ingest, curate, and quality-check motion datasets, and use LLMs to generate synthetic sensor data and accelerate ground-truth annotation Define benchmarks that measure model quality, reliability, and real-world behavior Apply modern ML techniques, including foundation models and multimodal approaches, to motion sensing, sensor fusion, and activity recognition Partner with hardware, apps, and platform teams to ship AI-driven experiences from the ground up Own the quality bar for motion features and turn the latest AI capabilities into real impact for users
Minimum Qualifications
B.S. in Computer Science or relevant field Experience building and shipping ML or AI systems, in production or through significant project work Hands-on experience with LLMs, prompt engineering, and agentic frameworks Strong software engineering fundamentals, with proficiency in Python and comfort across the stack Experience designing evaluation frameworks, quality metrics, and test infrastructure Demonstrated ability to work cross-functionally and deliver end-to-end features against release schedules
Preferred Qualifications
Experience with sensor data, motion algorithms, or embedded ML systems Familiarity with distributed compute platforms and large-scale data infrastructure Background in algorithm validation, simulation, or synthetic data generation
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