Job description

As an engineer, you will enable the next-generation of agentic ML product development systems at scale using Apple Foundation Models. You will sit at the intersection of cutting-edge research and product reality, bridging the gap between raw model performance and the nuanced needs of Apple customers worldwide. You will explore, design, and implement emerging techniques, ensuring alignment with product goals, privacy requirements, and performance metrics in a hands-on role. The role requires technical depth across ML & NLP, with a solid understanding of building scalable ML pipelines. You will redefine the ML development process across features and languages: problem formulation, experimentation, evaluation, fine-tuning, and continuous improvement that expand both the depth of Apple Intelligence’s capabilities and the breadth of its support for our global customer base. Masters or PhD in Computer Science, Electrical Engineering, Physics, Statistics or related field; or equivalent practical experience Prior experience with technical leadership or engineering management Strong foundation in Data Science and ML Stack. Familiarity with product ML/NLP lifecycle Familiarity with techniques such as SFT, RLHF, Data Synthesis, Parameter-Efficient Fine-Tuning, LLM-judge evaluation Excellent communication skills Experience with deploying large ML models for real world products and leading high-performing teams Experience curating, filtering, and synthesizing high-quality training datasets at scale Familiarity with ML pipelines that need to scale across languages Experience developing and training models for agentic workflows, tool calling and advanced reasoning techniques Familiarity with working and managing complex, large-scale codebases, with a strong emphasis on writing high-quality, maintainable, and well-tested code. Experience using AI-assisted development tools (e.g., Claude, Copilot, or similar) to accelerate experimentation, code development, and research workflows

Requirements

Must-haves

Masters or PhD in Computer Science, Electrical Engineering, Physics, Statistics or related field; or equivalent practical experience
Prior experience with technical leadership or engineering management
Strong foundation in Data Science and ML Stack.
Familiarity with product ML/NLP lifecycle
Familiarity with techniques such as SFT, RLHF, Data Synthesis, Parameter-Efficient Fine-Tuning, LLM-judge evaluation
Excellent communication skills
Experience with deploying large ML models for real world products and leading high-performing teams
Experience curating, filtering, and synthesizing high-quality training datasets at scale
Familiarity with ML pipelines that need to scale across languages
Experience developing and training models for agentic workflows, tool calling and advanced reasoning techniques
Familiarity with working and managing complex, large-scale codebases, with a strong emphasis on writing high-quality, maintainable, and well-tested code.
Experience using AI-assisted development tools (e.g., Claude, Copilot, or similar) to accelerate experimentation, code development, and research workflows

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

mlllmai

Platform: apple
Effort: medium