Research Scientist, AI Evaluation Science

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. Advance evaluation methodology through original research in one or more of the following areas: preference learning and reward modeling (RLHF, DPO, reward hacking mitigation); LLM-as-judge calibration, rubric design, and bias detection; intelligent evaluation strategies including active learning for test selection and automated failure discovery; or validity frameworks for evaluators (construct validity, transfer learning). You are not expected to cover all of these—depth matters more than breadth. Publish at top-tier venues (NeurIPS, ICML, ICLR, ACL, EMNLP), contributing to evaluation science as a recognized research area and representing Apple in the research community. Translate research into production-ready tools by partnering with platform engineers to productionize your methods into evaluation SDKs and APIs used across Apple. Collaborate with measurement scientists to integrate psychometric methods and validity frameworks into evaluation systems, ensuring evaluators measure what they claim to measure. Define the team's research agenda for evaluation science by identifying high-leverage open problems, validating that they address real-world challenges faced by ML engineers across Apple, and designing rigorous experimental programs to solve them. 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 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

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