ML Engineer Automated Evaluation and Adversarial Design

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 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.n

Day-to-day work involves designing building and maintaining automated evaluation systems that assess AI feature quality at scale including multi-turn conversation evaluation and end-to-end agent workflow testing. This includes creating adversarial test suites that probe model weaknesses and running stress tests to ensure features perform under demanding conditions with particular focus on failure modes that only emerge across extended interactions such as: context degradation goal drift and compounding deliverables include: evaluation frameworks and rubrics quality assessment reports adversarial test case libraries multi-turn stress-test pipelines and recommendations on model readiness.

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 experiencesnBuild 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 tasksnDevelop 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 sequencesnEnsure alignment between automated and human evaluation methods on an ongoing basis identifying and resolving systematic disagreementsnCollaborate with engineering partners to integrate evaluation into development and release workflowsnScale adversarial test case generation and stress test execution leveraging automation where appropriate including programmatic generation of multi-turn conversation scenarios and agent interaction tracesnInfluence model and feature quality decisions by communicating evaluation findings and readiness assessments to cross-functional partners

Bachelors degree in Computer Science Machine Learning Statistics or a related field n4 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 nExperience 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 nExperience designing adversarial or red-teaming test methodologies for ML models or AI-powered features including adversarial scenarios that target failures across multi-turn interactions nExperience with Python and ML frameworks (PyTorch TensorFlow or equivalent) in production or near-production settings nTrack record of owning technical direction for evaluation efforts across multiple features or product areas

Experience evaluating user-facing AI features in consumer applications with an understanding of how technical metrics connect to user-perceived quality nFamiliarity with productivity software or creative tools with the ability to assess output quality from a user workflow perspective nExperience ensuring alignment between automated and human evaluation methods including inter-annotator agreement analysis and bias detection nTrack record of designing evaluation systems that scale across multiple features or product areas without requiring bespoke solutions for each nExperience evaluating different types of AI systems including API-based and custom-trained models nDemonstrated ability to communicate evaluation findings and readiness assessments to cross-functional partners nExperience leveraging automation to scale evaluation data generation and analysis nExperience building evaluation pipelines for conversational AI dialogue systems or agentic workflows including turn-level and session-level automated scoring nFamiliarity 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 nExperience designing adversarial tests for tool-use reliability function-calling accuracy or agent planning quality nGraduate degree in a relevant field