Machine Learning Scientist - Apple Services Engineering, GenAI & ML Frameworks
Required skills
- LLM
- GenAI
- foundation models
- agentic reinforcement learning
- agentic system optimization
- domain-adaptive continual pretraining
- post-training
- preference optimization
- RL
- GRPO-style methods
Nice to have
- synthetic data generation
- eval
- training pipelines
Role summary
Apple Services GenAI & ML Frameworks team is seeking a Machine Learning Scientist to bridge foundation model capabilities with real-world production systems. This role focuses on LLM continual pretraining, posttraining, agentic reinforcement learning, and agentic system optimization. The ideal candidate will improve LLM domain knowledge, tool use, reasoning, and system integration, working closely with product, infra, and foundation model teams to deploy cutting-edge models at scale. Responsibilities include end-to-end model development and production integration, with expertise in LLM training, agentic systems, and deployment-aware optimization. Experience building tooling for synthetic data generation, evaluation, and training pipelines is strongly preferred.
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. 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.
Sample Apple interview questions
- 1
Design a typeahead box for a search engine
system designmedium - 2
Design a rate limiter
system designmedium - 3
Architect a key-value store for distributed systems.
system designmedium - 4
Design a system to schedule jobs in a distributed environment
system designmedium - 5
What is an API and how does it work?
technicalmedium
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