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Apple

Machine Learning Scientist - Apple Services Engineering, GenAI & ML Frameworks

Apple New York, New York, United States

Computers and Electronics Manufacturing · 10,001+ employees

4 h ago
machine-learning Senior (5-10 yrs) Full-time United States
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About the role

The role involves bridging foundation model capabilities with production systems through LLM training, agentic system optimization, and deployment-aware engineering. You will lead cross-functional initiatives to integrate cutting-edge models into user-facing features while ensuring production readiness and scalability.

What they look for

Large Language Models Python Deep Learning PyTorch Jax Tensorflow Natural Language Processing Reinforcement Learning Distributed Systems Synthetic Data Generation Agentic Systems Model Training Latency Optimization Evaluation Harnesses System Integration

Requirements

Candidates must possess a BS, MS, or PhD in a quantitative field and demonstrate proficiency in Python and deep learning frameworks like PyTorch, Jax, or Tensorflow. A proven track record in training large-scale models or building distributed systems is essential for this position.

Full description

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, 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.

Minimum Qualifications

BS/MS in a quantitative field, including Computer Science, Maths, Statistics, Physics, etc. 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.

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