Evaluation & Insights Engineer

Imagine what you could do here. At Apple, great new ideas have a way of becoming extraordinary products, services, and customer experiences very quickly. Bring passion and dedication to your job and there's no telling what you could accomplish!

Are you passionate about music, movies, and the world of Artificial Intelligence and Machine Learning? So are we! Join our Human-Centered AI team for Apple Products. In this role, you'll represent the user perspective on new features, review and analyze data, and evaluate AI models powering everything from search and recommendations to other innovative features. Collaborate with Data Scientists, Researchers, and Engineers to drive improvements across our platforms.

Description

We are looking for a Evaluation & Insights Engineer Human-Centered AI team to help evaluate and improve AI systems by combining data science, model behavior analysis, and qualitative insights. In this role, you will analyze AI outputs, develop evaluation frameworks, design qualitative, and translate findings into actionable improvements for product and engineering teams. This role blends deep technical expertise with strong analytical judgment to assess, interpret, and improve the behavior of advanced AI models. You will work cross-functionally with the Engineering and Project Managers, Product, and Research teams to ensure that AI experience is reliable, safe, and aligned with human expectations.","responsibilities":"AI Evaluation & Data Analysis

Lead complex evaluations of model behavior, identifying issues in reasoning, factuality, interaction quality, safety, fairness, and user alignment.

Build evaluation datasets, annotation schemas, and guidelines for qualitative assessments.

Develop qualitative + semi-quantitative scoring rubrics for measuring human-perceived quality (e.g., helpfulness, factuality, clarity, trustworthiness).

Run structured evaluations of model iterations and summarize strengths/weaknesses based on qualitative evidence.

Data Science & Modeling

Collaborate with model developers to refine model behavior using findings from qualitative outputs.

Use statistical and computational methods to identify patterns in qualitative data (e.g., assigning loss patterns, error taxonomies, thematic categorization).

Build dashboards, scripts, or workflows that codify evaluation metrics and automate portions of qualitative assessments.

Integrate qualitative evaluations with quantitative metrics (e.g., Precision@k, MRR, perplexity, accuracy, performance KPIs).

Framework & Pipeline Development

Create scalable pipelines for reviewing, annotating, and analyzing model outputs.

Define evaluation frameworks that capture nuanced human factors (e.g., uncertainty, trust calibration, conversational quality, interpretability).

Develop processes to track feature quality and model performance over time and flag regressions.

Cross-Functional Collaboration

Communicate evaluation results clearly to data scientists, engineers, and PMs.

Translate qualitative findings into clear loss patterns and actionable insights

Work with product teams to ensure AI behaviors align with real-world user expectations.

Preferred Qualifications

Experience working directly with LLMs, generative AI systems, or NLP models.

Familiarity with evaluations specific to AI safety, hallucination detection, or model alignment.

Experience designing annotation tasks or working with human labelers.

Understanding of mixed-method analysis (qualitative + quantitative).

Experience building internal tools, scripts, or dashboards for evaluation workflows.

Familiarity with prompt engineering, RAG systems, or model fine-tuning.

Experience evaluating LLMs, multimodal models, or other generative AI systems at scale.

Expertise in designing annotation guidelines and managing large annotation teams or vendors.

Background in human factors, social science, or qualitative assessment methodologies.