Senior Multilingual LLM Research Engineer

Apple, Inc.; New York (NY); On-site; USD 185,000 - 325,000; Full time

Benefits offered by this job

Medical and dental coverage

Retirement benefits

Employee stock purchase plan

Tuition reimbursement

Relocation assistance

Bonuses/commission (at Apple)

Job summary

Apple, Inc. is seeking a Senior ML Research Engineer to advance multilingual LLMs within the Translation & Multilingual Intelligence team in New York City.

You will shape post-training strategies, improve instruction following and tool use, and design architectures that deliver privacy-forward experiences across Apple products. You will collaborate with world-class researchers to push language coverage, evaluate models, and translate product requirements into model capabilities while maintaining

Qualifications

• 4+ years of ML/NLP experience focused on LLMs, post-training, or RL.
• Proficient in Python and a major DL framework (PyTorch or JAX).
• Master's/PhD or equivalent practical experience in Computer Science, ML, or related field.

Responsibilities

• Interface with product teams to translate requirements into model capabilities and improve training loss patterns.
• Research data strategies for high-quality human and synthetic data and curriculum learning for multilingual coverage.
• Design robust evaluation methodologies to measure model usefulness, factuality, and real-world performance.
• Develop end-to-end post-training strategies to unlock model capabilities and desired behaviors.

Skills

ML & NLP

Python

DL Frameworks (PyTorch/JAX)

Education

Master's or PhD in CS/ML

Tools

Python

Bash

Java/C/C++

Job description

Apple, Inc. is seeking a Senior ML Research Engineer to advance multilingual LLMs within the Translation & Multilingual Intelligence team in New York City.

You will shape post-training strategies, improve instruction following and tool use, and design architectures that deliver privacy-forward experiences across Apple products. You will collaborate with world-class researchers to push language coverage, evaluate models, and translate product requirements into model capabilities while maintaining