Job Details

Role Number: 200631663-3337

Summary

As part of Apple's AI and Machine Learning org, we encourage and create groundbreaking technology for large-scale ML systems, computer vision, natural language processing, and multi-modal understanding and generation. The Data and Machine Learning Innovation (DMLI) team is looking for a passionate Machine Learning Engineer to explore new methods, challenge existing metrics or protocols, and develop new insightful practices that will change how we understand data and overcome real-world ML challenges.
As a team member, you will work on some of the most ambitious technical challenges in the field. Your role will involve collaborating closely with our machine learning researchers, engineers, and data scientists. Together, you will spearhead groundbreaking research initiatives and develop transformative products designed to build a significant impact for billions of users worldwide!

Description

As a Machine Learning Engineer for LLM Agent and Reinforcement Learning, you will play a pivotal role in shaping the next generation of intelligent agent systems that power Apple Intelligence. You will develop advanced LLM-based agents capable of reasoning, planning, and acting across multi-turn tasks and tool-use environments. Your work will directly influence Apple’s foundation models and agentic intelligence capabilities across products and platforms.

You will collaborate with cross-functional research and engineering teams to push the frontier of agent modeling, mid-training, reinforcement learning, and system-level orchestration. Your responsibilities will include designing innovative mid-training and post-training pipelines, scaling large-scale synthetic data generation for agent learning, and improving end-to-end agent behaviors through data-model co-design. You will also have the opportunity to publish and present your work at top ML and AI research venues.

Your work may span a variety of directions, including but not limited to:

• Develop and improve LLM-based agent models that can plan, reason, and act across complex, multi-turn tasks.
• Design mid-training pipelines to enhance agentic capabilities such as tool-use, reflection, and long-horizon reasoning.
• Explore and implement reinforcement learning and feedback-based fine-tuning to align agent behaviors with human and system objectives.
• Build simulation and evaluation environments for measuring multi-turn success rate, planning efficiency, and robustness of agent interactions.
• Develop scalable frameworks for synthetic agentic data generation, including user–assistant–tool trajectories, environment modeling, and self-play.
• Collaborate with modeling, data, and infrastructure teams to integrate new training signals and improve overall system performance.
• Stay on top of cutting-edge research in agentic LLMs, reasoning, tool-use, mid-training, and reinforcement learning.

Minimum Qualifications

• Strong background in machine learning, natural language processing, or reinforcement learning with a focus on LLM or agentic systems.

• Solid understanding of LLM training paradigms (pre-training, mid-training, RL, post-training) and multi-modal or tool-use-enhanced architectures.

• Excellent programming skills and experience with Python and modern deep learning frameworks (PyTorch, JAX, or equivalent).

• Ability to design, run, and analyze large-scale training and evaluation experiments.

• M.S. in Computer Science, Machine Learning, Artificial Intelligence, or related fields.

Preferred Qualifications

• 3+ years of hands-on experience developing agentic LLMs, tool-use systems, or RL-based fine-tuning pipelines.

• Experience in multi-turn data generation, simulation environments, or agent evaluation frameworks.

• Deep familiarity with self-improvement loops, reward modeling, or interactive reinforcement learning.

• Strong publication record in top-tier venues (e.g., NeurIPS, ICLR, ICML, AAAI, ACL, EMNLP).

• Proven ability to work in collaborative, cross-functional ML teams.

• Ph.D. in Machine Learning, NLP, Reinforcement Learning, or related disciplines.

Apple is an equal opportunity employer that is committed to inclusion and diversity. We seek to promote equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics. Learn more about your EEO rights as an applicant (https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf) .