Annotation Data Scientist, Evaluation Integrity (Siri)

Play a part in the ongoing revolution in human-computer interaction. Siri is evolving - and the way we evaluate it has to evolve with it. Join the Evaluation Integrity team to help build the trusted quality signal behind every Siri release.\\nWithin the Siri evaluation organization, the Human Evaluation sub-team is responsible for answering the question: can we trust our evals? We do that by designing human-in-the-loop (HITL) annotation tasks that scrutinize every moving part of an agentic evaluation - the simulated user agent, the conversation it has with Siri, and the automated evaluators that grade the exchange. This role sits at the intersection of data science, human annotation engineering, and evaluation methodology, and is instrumental in turning human judgment into a rigorous, reproducible signal that directly informs pre-ship model and product decisions.\\n

As an Annotation Data Scientist on the Evaluation Integrity team, you will design and run HITL annotation projects that evaluate the quality and authenticity of agentic user personae, the validity of agent-to-agent conversations, and the reliability of LLM-as-judge and rule-based evaluators against Siri's product specifications. You will own annotation initiatives end-to-end; from rubric design and tooling, through annotator calibration, to data science analysis that turns annotator judgments into actionable signal for modeling, planning, and product teams.\n

Bachelor's or Master's degree in a quantitative or related field such as Data Science, Computer Science, Linguistics, Statistics, or Cognitive Science, or equivalent job-related experience.\n3 years of hands-on experience working with human-annotated datasets or human-in-the-loop evaluation methodologies for machine learning, natural language processing, or large language model systems.\n3 years of experience using Python for data processing, analysis, and prototyping, including experience with libraries such as pandas, Jupyter, and at least one data visualization library.\nExperience designing, implementing, and communicating annotation schemas, rubrics, or ontologies for machine learning training or evaluation data.\nExperience managing multiple concurrent dataset curation efforts, including scoping work, iterating on guidelines, coordinating with in-house or vendor annotators, and monitoring annotator performance metrics such as accuracy, throughput, and inter-annotator agreement.\nExperience specifying or designing custom annotation tooling in collaboration with software engineers.

Experience evaluating LLM-powered or agentic systems, including familiarity with LLM-as-judge methodologies, rubric-based grading, or trajectory and tool-call evaluation.\nFamiliarity with statistical methods that address accuracy and variability in human annotation data, such as inter-annotator agreement, Cohen's or Fleiss' kappa, Krippendorff's alpha, or bootstrapping.\nData-querying experience with SQL, Spark, or similar, and comfort working with large, complex, real-world datasets.\nExperience building pre-ship evaluation pipelines for conversational or assistant products.\nExperience with prompt engineering, or with designing simulated user personae for agent evaluation.\nExperience running annotation programs across multiple locales or at large scale.\nExcellent written and verbal communication skills, with the ability to explain technical topics clearly to data scientists, engineers, annotators, and cross-functional partners.\nProven ability to collaborate effectively across functions and drive projects of varying sizes and scopes - knowing when to dive deep and when to delegate.