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Machine Learning Engineer, Apple Services Engineering
Apple
📍
San Francisco, United States
Location
San Francisco
Posted
June 07, 2026
Commute
Local Area
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Job Description
**Weekly Hours:** 40
**Role Number:** 200653268-3577
**Summary**
Apple Services GenAI & ML Frameworks team aims at bridging foundation model capabilities with real-world production systems. The work spans LLM continual pretraining, posttraining, agentic reinforcement learning, agentic system optimization etc.. This role is part of the cross-LOB effort to support various GenAI use cases across ASE, and specializes in improving LLM domain knowledge, tool use, reasoning, and system integration—working closely with product, infra, and foundation model teams to bring cutting-edge models into user-facing features at scale.
**Description**
We are seeking a strong candidate who can operate end-to-end across model development and production integration—someone equally strong in (1) LLM training (domain-adaptive continual pretraining, post-training, preference optimization / RL such as GRPO-style methods), (2) agentic systems (tool schemas, multi-turn reliability, ru...
**Role Number:** 200653268-3577
**Summary**
Apple Services GenAI & ML Frameworks team aims at bridging foundation model capabilities with real-world production systems. The work spans LLM continual pretraining, posttraining, agentic reinforcement learning, agentic system optimization etc.. This role is part of the cross-LOB effort to support various GenAI use cases across ASE, and specializes in improving LLM domain knowledge, tool use, reasoning, and system integration—working closely with product, infra, and foundation model teams to bring cutting-edge models into user-facing features at scale.
**Description**
We are seeking a strong candidate who can operate end-to-end across model development and production integration—someone equally strong in (1) LLM training (domain-adaptive continual pretraining, post-training, preference optimization / RL such as GRPO-style methods), (2) agentic systems (tool schemas, multi-turn reliability, ru...