About the job
The Productivity and Machine Learning Evaluation team ensures the quality of AI-powered features across a suite of productivity and creative applications; including Creator Studio, used by hundreds of millions of people. This team serves as the primary evaluation function, providing critical quality signals that directly influence model development decisions and product launches. This role focuses on building and scaling automated evaluation systems and designing adversarial and stress-testing methodologies across multiple AI features. The work requires a deep understanding of how AI systems fail and how to measure quality rigorously. As features evolve from single-turn interactions into multi-turn, agentic experiences, the evaluation challenge shifts from assessing individual outputs to stress-testing entire conversation flows and agent decision chains. This is an opportunity to shape the evaluation infrastructure that determines whether AI features meet the bar for hundreds of millions of users.
Responsibilities
Define and own the automated evaluation approach for AI features, translating qualitative notions of quality into measurable, reproducible assessments across both single-turn and multi-turn agentic experiences
Build adversarial test suites that target known and emerging model failure modes, including edge cases relevant to productivity application workflows including conversation-level failures such as context loss, instruction forgetting, and cascading errors across multi-step tasks
Develop and execute stress test protocols that validate minimum performance thresholds under atypical input conditions including extended conversation lengths, adversarial mid-conversation topic shifts, and complex tool-use sequences
Ensure alignment between automated and human evaluation methods on an ongoing basis, identifying and resolving systematic disagreements
Collaborate with engineering partners to integrate evaluation into development and release workflows
Scale adversarial test case generation and stress test execution, leveraging automation where appropriate, including programmatic generation of multi-turn conversation scenarios and agent interaction traces
Influence model and feature quality decisions by communicating evaluation findings and readiness assessments to cross-functional partners
Qualifications
Minimum
Bachelor’s degree in Computer Science, Machine Learning, Statistics, or a related field
4+ years of experience building or significantly extending ML evaluation systems, including designing evaluation benchmarks or quality assessment frameworks including evaluation of sequential or multi-step AI outputs
Experience independently defining evaluation architecture and methodology for AI or ML systems with the ability to design evaluation approaches where the unit of analysis is a conversation or session rather than a single output
Experience designing adversarial or red-teaming test methodologies for ML models or AI-powered features including adversarial scenarios that target failures across multi-turn interactions
Experience with Python and ML frameworks (PyTorch, TensorFlow, or equivalent) in production or near-production settings
Track record of owning technical direction for evaluation efforts across multiple features or product areas
Preferred
Experience evaluating user-facing AI features in consumer applications, with an understanding of how technical metrics connect to user-perceived quality
Familiarity with productivity software or creative tools, with the ability to assess output quality from a user workflow perspective
Experience ensuring alignment between automated and human evaluation methods, including inter-annotator agreement analysis and bias detection
Track record of designing evaluation systems that scale across multiple features or product areas without requiring bespoke solutions for each
Experience evaluating different types of AI systems, including API-based and custom-trained models
Demonstrated ability to communicate evaluation findings and readiness assessments to cross-functional partners
Experience leveraging automation to scale evaluation data generation and analysis
Experience building evaluation pipelines for conversational AI, dialogue systems, or agentic workflows, including turn-level and session-level automated scoring
Familiarity with agent orchestration frameworks (LangChain, LangGraph, CrewAI, AutoGen) and observability tooling (LangSmith, Braintrust, Arize), with an understanding of how to instrument and evaluate multi-step agent runs
Experience designing adversarial tests for tool-use reliability, function-calling accuracy, or agent planning quality
Graduate degree in a relevant field