AIML - Senior Machine Learning Research Engineer, LLM Post-training (Multilinguality)

Summary

The Apple Translation & Multilingual Intelligence team is looking for exceptional researchers/scientists to develop the next generation of Apple Foundation Models to allow our users to communicate across language barriers and perform multilingual tasks. You will tackle core training challenges in instruction following, tool use, deep reasoning, and architectural adaption — designing models that deliver magical, deeply integrated, and privacy-forward experiences across the Apple ecosystem. You will work alongside a fast-growing team of world-class experts to explore novel training strategies, architectural adaptations, increasing language coverage, and advance evaluation methodologies.

Passionate about Natural Language Processing, Large Language Modeling (LLMs) and multilinguality? Join us to bring the most advanced multilingual solution across the Apple ecosystem.

Description

In this role, you will play a critical role shaping the future of our LLM efforts, specifically in transforming our models into highly capable, intelligent assistants that power billions of Apple products. You will tackle core training challenges in instruction following, tool use, deep reasoning, and architectural adaption — designing models that deliver magical, deeply integrated, and privacy-forward experiences across the Apple ecosystem. You will work alongside a fast-growing team of world-class experts to explore novel training strategies, architectural adaptations, and advanced evaluation methodologies.

Responsibilities

• * Interface with product teams to translate product requirements into model capabilities, drive the research and development to improve observed loss patterns in different model training stages.
• * Drive data strategy by researching methods for high-quality human and synthetic data generation, automated data filtering, and curriculum learning to improve multilingual response quality and locale coverage.
• * Design robust evaluation methodologies to measure model helpfulness, factuality, and utility, moving beyond static benchmarks to accurately capture real-world performance.
• * Design and iterate on end-to-end post-training strategies to unlock model capacities toward achieving specific model behaviors.

Minimum Qualifications

• 4+ year experience in machine learning and NLP with a focus on LLMs, post-training, or reinforcement learning, backed by a strong record of academic or real-world accomplishments in these or closely related domains.
• Proficient programming skills in Python and a major deep learning framework such as JAX or PyTorch.
• Masters/PhD, or equivalent practical experience, in Computer Science, Machine Learning, or a related technical field.

Preferred Qualifications

• Experience developing state-of-the-art LLMs that are natively multilingual and can successfully complete a wide range of tasks in a variety of different languages.
• Experience training large models at scale, with familiarity in distributed training challenges and trade-offs.
• Ability to formulate a research problem, design, experiment and implement solutions in Python, Bash, Java/C/C++
• Design and deployment of real-world, large-scale, user-facing LLM or Machine Translation systems.
• Strong communication skills and a passion for working cross-functionally across Research and Product teams.