Senior Applied Scientist, Multilingual AI Evaluation

DESCRIPTION In this role, you’ll help ensure Apple’s AI features work well across languages and cultures. Your goal is to make our evaluation tooling multilingual from the start so that engineers building AI features can design, test, and ship across the world from day one. It’s a broad applied science role: you’ll shape how Apple evaluates AI wherever the hardest questions are, and you’ll have the opportunity to publish novel work. The scientific challenge is real. How do we ensure we consistently evaluate AI features across different grammar, script, or cultural norms and how do we do this at scale? You’ll bring linguistic judgment to questions like these and, working with measurement scientists and ML researchers, turn it into validated methodology that holds across dozens of languages. This is a hands-on role. You’ll design and implement your own methods in Python, working closely with research and engineering partners, while staying focused on the science of getting evaluation right. MINIMUM QUALIFICATIONS MS in Linguistics, Computational Linguistics, NLP, Computer Science, or a related field — or equivalent research/work experience. Deep expertise in linguistics, with working fluency in the structure of multiple languages beyond English. Strong proficiency in Python. Solid understanding of LLMs and AI evaluation fundamentals, including how language models process and generate across languages. Demonstrated experience shipping or evaluating features across multiple languages or locales. Experience designing benchmarks, datasets, or human evaluation protocols, with attention to statistical rigor and reproducibility. Ability to drive initiatives independently and collaborate across a cross-functional, interdisciplinary team. Strong written and verbal communication skills. PREFERRED QUALIFICATIONS PhD in Linguistics, Computational Linguistics, or NLP with a focus on multilingual or cross-lingual modeling. Publications in NLP, multilingual evaluation, or evaluation methodology. Hands-on experience with modern ML frameworks (PyTorch, JAX) and with fine-tuning or evaluating LLMs. Experience with low-resource languages, dialectal variation, or sociolinguistics. Familiarity with localization/internationalization workflows and quality assessment. Experience with LLM-as-judge approaches, rubric design, or bias and fairness evaluation across languages. Fluency or professional proficiency in one or more languages in addition to English. #J-18808-Ljbffr Socket.dev