The Siri NL team is creating groundbreaking technology for machine learning, natural language understanding, and artificial intelligence. The features we create are redefining how hundreds of millions of people use their computers and mobile devices in their day-to-day tasks. As a machine learning language engineer, you will have the opportunity to directly impact Siri's ability to understand what people want by designing and building the standards for Siri's language understanding systems.
Description
Develop ontologies, models, and standards to define and organize Siri's understanding.
Create, validate, and review requests for ontology changes.
Develop data strategies for training and evaluating models including human-authored/annotated data and synthetic data.
Own requirements related to ontology tools, metrics, and process improvements.
Work with cross-functional teams to gather requirements across components and negotiate inter-team constraints.
Minimum Qualifications
Bachelors degree or higher in Computer Science, Linguistics, or science / mathematics degrees.
Minimum 3 years experience of product development working with data annotation and creating ontologies and taxonomies.
Experience with data-driven decision making.
Experience with statistics and data science, including database queries.
Experience with Python, Swift, C++, or other equivalent programming languages and AI-assisted programming.
Background in computational linguistics, data science, or technical product management.
Preferred Qualifications
A curiosity about natural language understanding and its associated complex problems such as the representation of ambiguity.
Experience working cross-functionally with machine learning and product engineering teams, data science teams, annotation teams, and designers.
Demonstrated experience with systems thinking, particularly gathering requirements, identifying and resolving conflicts.
Enthusiasm working on a diverse set of hard problems in a fast-moving product development environment.
Familiarity with lexical semantics.
Eeo Content
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