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ML Engineer - Automated Evaluation and Adversarial Design
Apple
H1B ✓On-sitesenior levelPosted April 22, 2026
About the Role
Design, build, and maintain automated evaluation systems to assess AI feature quality at scale, including multi-turn conversation and agent workflow testing. Develop adversarial test suites and stress-testing methodologies to identify model weaknesses and ensure performance across extended interactions.
Requirements
Requires a Bachelor's degree in Computer Science, Machine Learning, or a related field and 4+ years of experience in building ML evaluation systems. Candidates must demonstrate proficiency in Python, ML frameworks, and experience with adversarial testing or red-teaming for AI models.
Full Job Description
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.
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 errors. Typical deliverables include: evaluation frameworks and rubrics, quality assessment reports, adversarial test case libraries, multi-turn stress-test pipelines, and recommendations on model readiness.
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
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
Description
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 errors. Typical deliverables include: evaluation frameworks and rubrics, quality assessment reports, adversarial test case libraries, multi-turn stress-test pipelines, and recommendations on model readiness.
Minimum Qualifications
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 Qualifications
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