Machine Learning Scientist - GenAI & ML Frameworks

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.

Minimum Qualifications

BS/MS in a quantitative field, including Computer Science, Maths, Statistics, Physics, etc. 3+ years of relevant work experience Proficient programming skills in Python Hands-on experience working with deep learning toolkits such as Jax, Tensorflow or PyTorch Proven track record in training or deployment of large models or building large-scale distributed systems Deep understanding of Deep Learning and Large Language Models (LLMs) Natural Language Processing

Preferred Qualifications

PhD in a quantitative field, including Computer Science, Maths, Statistics, Physics, etc.

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, rubric- or verifier-based learning loops), and (3) deployment-aware optimization (latency/cost/reliability tradeoffs, evaluation harnesses, and iterative improvement from production signals). The ideal candidate has a track record of turning LLM research into shipped capabilities, can partner effectively with product, infra, and foundation model teams, and can lead ambiguous cross-LOB initiatives from problem definition through execution and scaling. Experience building robust tooling around synthetic data generation, eval, and training pipelines for LLMs is strongly preferred, since this role is expected to raise the bar on both research velocity and production readiness.