About the job
We are in search of a driven Machine Learning Engineer who has a strong understanding of LLMs and Generative AI and is excited to explore the rapidly evolving landscape of LLM-powered agents, tool-use models, and advanced reasoning techniques. You will help translate groundbreaking research into production systems, influence our technical direction, and shape the future of AI at Apple.
Agentic AI is an emerging area for our team—prior experience is a plus but not required. What matters most is curiosity, strong ML fundamentals, and the ability to navigate and apply cutting-edge research!
Responsibilities
Leading exploration and application of LLMs, Generative AI, and emerging agentic AI techniques (e.g., tool-use models, model-driven planning, task decomposition).
Translating the latest research into high-performing, production-ready ML systems that enhance user experiences across Apple products.
Helping define the technical direction for LLM-driven and agentic AI capabilities within the Intelligence Platform.
Contributing to all phases of model development—problem formulation, experimentation, evaluation, fine-tuning, deployment, and continuous improvement.
Collaborating cross-functionally to design and implement ML solutions, ensuring alignment with product goals, privacy requirements, and performance metrics.
Driving innovation by identifying opportunities to incorporate advanced reasoning, grounding, or agentic patterns into existing ML systems.
Qualifications
Minimum
Advanced degree (Master’s or Ph.D.) in Computer Science, Machine Learning, Artificial Intelligence, or a related field.
Strong experience in Machine Learning, with emphasis on LLMs, Generative AI, or large-scale deep learning systems.
Demonstrated ability to read, interpret, and apply cutting-edge research to real-world engineering problems.
Experience with model training, fine-tuning, or building scalable ML systems.
Strong programming and problem-solving skills.
Preferred
Familiarity with agentic AI, structured tool-use models, or multi-turn reasoning systems. (Not required—experience is a plus, and interest in the domain is highly valued.)
Hands-on experience with end-to-end LLM development: dataset curation, fine-tuning, evaluation, prompt optimization, or model hosting.
Published research demonstrating contributions to LLMs, Generative AI, or related subfields.
Ability to guide technical direction, mentor others, and collaborate effectively in a fast-paced environment.