Annotation Data Scientist, Evaluation Integrity (Siri)

About the role

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.

Responsibilities

• Manage multiple annotation programs in parallel. Plan, scope, and manage human evaluation tasks end-to-end - requirements gathering, annotator coordination, vendor management, timeline tracking, and stakeholder delivery.
• Design custom annotation tooling in partnership with software engineers. Prototype task UIs, specify tool requirements, and collaborate with tooling engineers on the annotation platforms the Human Evaluation team relies on.
• Apply data science rigor to human-labeled data. Use Python to build analysis pipelines that measure evaluator accuracy against the annotator pool, surface discrepancies between LLM-judge and rule-based evaluators, and quantify the reliability of each agentic evaluator as a source of truth.
• Turn annotator feedback into evaluator improvements. Close the loop between annotators and the data scientists and software engineers who own user agents and automated evaluators, feeding findings back into prompts, rubrics, and product guidelines.
• Contribute to the organization-wide eval health story. Partner with the User Feedback and Eval Science sub-team to ensure human signal is represented in the eval health report delivered to leadership.

Requirements

• Experience evaluating LLM-powered or agentic systems, including familiarity with LLM-as-judge methodologies, rubric-based grading, or trajectory and tool-call evaluation.
• Familiarity 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.
• Data-querying experience with SQL, Spark, or similar, and comfort working with large, complex, real-world datasets.
• Experience building pre-ship evaluation pipelines for conversational or assistant products.
• Experience with prompt engineering, or with designing simulated user personae for agent evaluation.
• Experience running annotation programs across multiple locales or at large scale.
• Excellent written and verbal communication skills, with the ability to explain technical topics clearly to data scientists, engineers, annotators, and cross-functional partners.
• Proven ability to collaborate effectively across functions and drive projects of varying sizes and scopes - knowing when to dive deep and when to delegate.
• 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.
• 5+ 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.
• 5+ years of experience using Python for data processing, analysis, and prototyping, including experience with libraries

Additional Information

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. Within 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.

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Company Intel

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