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Principal Applied Research Engineer/Scientist

Apple
Posted 2 months ago, valid for 21 days
Location

Cupertino, CA 95015, US

Salary

Competitive

Contract type

Full Time

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Sonic Summary

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  • Apple is seeking a Principal Applied Research Engineer to innovate in self-improving systems and personalized autonomous multi-agent systems.
  • Candidates should have an MSc or PhD in Computer Science, Machine Learning, or a related field, along with significant practical experience in LLM productization.
  • The role requires deep expertise in transformer-based LLMs and multi-modal foundation models, particularly in integrating text, vision, and audio.
  • Candidates should possess hands-on mastery of the model lifecycle, including pre-training, fine-tuning, optimization, and safety alignment.
  • The position offers a competitive salary, and a minimum of 5 years of experience is preferred.
At Apple, we don’t just build products; we create experiences that enrich the lives of billions. You have the unique opportunity to seamlessly integrate foundational AI research with world-class hardware and software, bringing cutting-edge technology to billions of users. Your work will have a far-reaching impact, setting precedents for how autonomous, deeply personalized AI technologies enrich lives across our entire product ecosystem.

Description


We are seeking a Principal Applied Research Engineer to drive breakthrough innovation across self-improving systems, agentic memory, and deeply personalized autonomous multi-agent systems. You will shape next-generation intelligent architectures that learn continuously, retain and reason over persistent memory, coordinate across agents, and translate frontier research into scalable product experiences. This unique opportunity places you at the forefront of innovation, working on projects that redefine user experiences through advanced machine learning.

Minimum Qualifications


MSc or PhD in Computer Science, Machine Learning, or related field; or equivalent practical experience delivering zero-to-one LLM productization. Deep expertise in transformer-based LLMs and multi-modal foundation models, with a focus on integrating text, vision, and audio with cross-modal attention. Hands-on mastery across the model lifecycle, including pre-training, fine-tuning, performance optimization, and safety alignment. Expertise in designing AI agents for complex reasoning while navigating the unique challenges of on-device compute constraints.

Preferred Qualifications


Demonstrated ability to influence high-level architecture decisions and effectively advocate for novel research directions to product leadership.



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