ML Engineer - Automated Evaluation and Adversarial Design

Apple • San Diego, CA

21d

About The Position

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.

Requirements

• 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.
• Experience evaluating sequential or multi-step AI outputs.
• Experience independently defining evaluation architecture and methodology for AI or ML systems.
• 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.
• Experience with 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.

Nice To Haves

• 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.

Responsibilities

• 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.
• Creating adversarial test suites that probe model weaknesses.
• 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.
• Developing evaluation frameworks and rubrics.
• Producing quality assessment reports.
• Creating adversarial test case libraries.
• Building multi-turn stress-test pipelines.
• Providing recommendations on model readiness.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior