Research Scientist, AI Evaluation Science

Apple • Seattle, WA

1d

About The Position

AI systems are only as trustworthy as the methods used to evaluate them. At
Apple, where AI powers experiences for billions of people, getting evaluation
right is not a support function—it is a foundational science. Our team, part of
Apple Services Engineering, is building that scientific foundation: rigorous,
scalable evaluation methodology for LLMs, agentic systems, and human-AI
interaction. What makes this team unusual is its interdisciplinary core. You
will work alongside measurement scientists (psychometrics, validity theory), ML
researchers, and platform engineers—bringing together ML research, statistical
rigor, and production engineering. We are looking for a Research Scientist who
treats evaluation methodology itself as a first-class research problem—someone
with deep technical fluency in preference learning, reward modeling, or
calibration theory, and the drive to advance the field while solving real
problems at scale. We're hiring at multiple levels (early-career to senior
researchers). What unites all candidates is depth of thinking about evaluation
as a research problem.

DESCRIPTION

This is primarily a research role. You will formulate open problems in
evaluation science, design experiments, publish findings, and drive projects
from conception through completion. While you will also partner with platform
engineers to ensure your methods are productionized into SDKs and APIs, the
focus of the role is original research. Our research team brings together ML
scientists and measurement scientists to tackle evaluation as both a machine
learning and a measurement problem, building methods that are technically
innovative and scientifically valid. You will also work closely with a platform
engineering team that translates research into production-ready SDKs and APIs
used across Apple. The successful candidate will have a strong publication
record in evaluation-adjacent ML areas and a demonstrated ability to implement
complex methods from recent papers, run large-scale experiments, and communicate
results to both technical and non-technical audiences.

Requirements

• Ph.D. in Computer Science, Machine Learning, or a closely related field, with a research focus in evaluation-adjacent areas (preference learning, RLHF, human feedback, calibration, automated assessment)
• Strong publication record at top-tier conferences (NeurIPS, ICML, ICLR, ACL, EMNLP), including first-author publications demonstrating independent research contributions
• Deep technical expertise in at least one evaluation-adjacent ML area, with strong mathematical foundations: preference learning and reward modeling (RLHF, DPO, reward hacking, specification gaming); OR calibration theory, proper scoring rules, and statistical reliability; OR human-AI interaction methodology (active learning, annotation quality, preference elicitation)
• Demonstrated ability to implement complex methods from recent papers and run large-scale experiments
• Track record of translating research into practical systems—prototypes, tools, or methods adopted by others
• Excellent written and verbal communication skills, including the ability to write clear research papers and explain complex concepts to diverse audiences

Nice To Haves

• Publications specifically on evaluation methodology—papers about how to evaluate, not just papers that use evaluation to demonstrate model improvements
• Strong hands-on experience with modern ML frameworks (PyTorch, JAX, or TensorFlow) and training or fine-tuning large language models
• Experience with theoretical foundations of evaluation: measurement theory and validity frameworks, statistical learning theory (calibration, reliability, decision theory), or preference elicitation and aggregation
• Specific research experience in one or more of: reward modeling and RLHF for alignment; LLM-as-judge approaches (calibration, rubric design, bias mitigation); benchmark design and validation (IRT, contamination detection); human evaluation methodology (protocol design, quality control); or agentic and multi-agent system evaluation
• Demonstrated passion for evaluation as a research area: conference presentations, workshops, or tutorials on evaluation topics; open-source contributions to evaluation tools or benchmarks; active engagement with the evaluation research community
• Experience with cross-disciplinary research, such as collaboration with social scientists, psychometricians, or domain experts

Responsibilities

• Formulate open problems in evaluation science
• Design experiments
• Publish findings
• Drive projects from conception through completion
• Partner with platform engineers to ensure methods are productionized into SDKs and APIs

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree