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

Apple

New York City, NYJobSeen 3w agoSeen in employer's feed 3 days ago

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At a glance

Compensation
No compensation found
Location
New York City, NY
Work Authorization
Not specified

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Job overview

Apple Services GenAI & ML Frameworks bridges foundation model capabilities with production systems across Apple Services Engineering. This scientist will improve LLM domain knowledge, tool use, reasoning, and system integration, working with product, infrastructure, and foundation model teams to bring models into user-facing features at scale. The role spans model training, agentic systems, and deployment-aware optimization, and leads cross-LOB initiatives from problem definition through execution and scaling.

Skills & qualifications

RequiredNice to have

Skills

PythonJaxTensorflowPytorchLarge Model TrainingLarge Model DeploymentDistributed SystemsDeep LearningLarge Language ModelsNatural Language ProcessingLLM TrainingAgentic SystemsDeployment OptimizationSynthetic Data GenerationEvaluation HarnessesTraining PipelinesCross-Functional CollaborationInitiative Leadership

Qualifications

BS/MS in Quantitative Field3+ Years Relevant Work ExperiencePhD in Quantitative Field

Full job description

Role Number: 200687461-2459

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, 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.

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.

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