4 jobs for "ML Engineer - Automated Evaluation and Adversarial Design"

ML Engineer - Automated Evaluation and Adversarial Design

At Apple - Culver City

Posted on Oct 15

View job Apply on company site

ML Engineer - Automated Evaluation and Adversarial Design

At Apple - Culver City

Posted on Oct 15

View job Apply on company site

ML Engineer - Automated Evaluation and Adversarial Design

At Apple - Culver City

Posted on Oct 15

View job Apply on company site

ML Engineer - Automated Evaluation and Adversarial Design

At Apple - Culver City

Posted on Oct 15

View job Apply on company site

Apple

ML Engineer - Automated Evaluation and Adversarial Design

Culver City, CA$142,300 – $263,300Posted 23 days ago

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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.

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.","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

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

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

Pay & Benefits

At Apple, base pay is one part of our total compensation package and is determined within a range. This provides the opportunity to progress as you grow and develop within a role. The base pay range for this role is between $142,300 and $263,300, and your base pay will depend on your skills, qualifications, experience, and location.

Apple employees also have the opportunity to become an Apple shareholder through participation in Apple's discretionary employee stock programs. Apple employees are eligible for discretionary restricted stock unit awards, and can purchase Apple stock at a discount if voluntarily participating in Apple's Employee Stock Purchase Plan. You'll also receive benefits including: Comprehensive medical and dental coverage, retirement benefits, a range of discounted products and free services, and for formal education related to advancing your career at Apple, reimbursement for certain educational expenses - including tuition. Additionally, this role might be eligible for discretionary bonuses or commission payments as well as relocation. Learn more about Apple Benefits

Note: Apple benefit, compensation and employee stock programs are subject to eligibility requirements and other terms of the applicable plan or program.

About Apple

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Industry: Technology: Manufacturing

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