This role pays less than
62%
of similar roles. Most pay
$187,850–$254,750
— the shaded band above.
At the midpoint, this role pays about
$203k
versus about
$221k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3587 open roles
on FindRole.
Listed pay typically runs
$165,800–$277,600
across 2768 roles with salary data.
ML Engineer - Automated Evaluation and Adversarial Design joins the Productivity and Machine Learning Evaluation team to ensure quality across AI-powered features. This role focuses on building and scaling automated evaluation systems, designing adversarial test suites, and developing stress-testing methodologies for multi-turn conversation flows and agentic experiences. The engineer will translate qualitative quality into measurable assessments, identify failure modes like context degradation or goal drift, and create robust evaluation frameworks and rubrics. Key responsibilities include managing automated and human evaluation alignment, scaling test case generation, and providing readiness assessments to cross-functional partners. Required skills include Python, PyTorch, or TensorFlow, along with experience in ML evaluation systems, red-teaming methodologies, and agentic workflows. The role addresses the technical challenge of evaluating complex, multi-step interaction chains rather than just single outputs within productivity and creative applications.
Define and own automated evaluation approaches that translate qualitative quality into measurable metrics for single-turn and multi-turn AI experiences.
Build adversarial test suites to identify model failure modes such as context loss, instruction forgetting, and cascading errors.
Develop and execute stress test protocols to validate performance under atypical conditions like extended conversation lengths and complex tool-use sequences.
Ensure alignment between automated and human evaluation methods by identifying and resolving systematic disagreements.
Scale the generation of adversarial test cases and stress tests using programmatic automation for multi-turn scenarios.
Communicate evaluation findings and model readiness assessments to cross-functional partners to influence product decisions.
What we're looking for
Bachelor's degree in Computer Science, Machine Learning, Statistics, or a related field.
4+ years of experience building or extending ML evaluation systems and designing quality assessment frameworks.
Experience independently defining evaluation architecture for AI systems where the unit of analysis is a conversation or session.
Experience designing adversarial or red-teaming test methodologies for ML models, specifically targeting multi-turn interactions.
Experience with Python and ML frameworks like PyTorch or TensorFlow in production or near-production settings.
Track record of owning technical direction for evaluation efforts across multiple features or product areas.
Graduate degree in a relevant field (preferred).
Experience evaluating user-facing AI features, productivity tools, or agentic workflows (preferred).
Machine Learning
Computer Vision
Python
Data Analysis
Statistical Modeling
Deep Learning
Data Pipelines
Visualization
Synthetic Data
Data Augmentation
Model Monitoring
Generative AI
Root-Cause Analysis
Evaluation Frameworks