Staff Generative AI Research Engineer, Reasoning & Memory - SIML

Apple • Cupertino, CA

2d

About The Position

The System Intelligence and Machine Learning (SIML) Content Understanding teams
are seeking a Staff Applied Researcher in Reasoning & Memory Systems. You will
be working alonside teams that are in charge of operating system wide
embeddings, personalized RAG workstreams, tool calling, context compaction /
efficiency & memory systems. Projects are focussed on advancing Apple
Intelligence capabilities, while working closely across disciplines with our
partners in hardware engineering, design and product. Selected references to our
prior work (a) https://arxiv.org/pdf/2507.13575, (b)
https://arxiv.org/pdf/2407.21075, (c)
https://www.apple.com/newsroom/2024/12/apple-intelligence-now-features-image-playground-genmoji-and-more/

We are seeking a candidate with a track record in algorithm development for
agentic reasoning & memory. Key attributes expected in the role are fluency in
algorithm development (prompt optimization and post training), strong expertise
with relevant techniques (reinforcement learning, multimodal reasoning), and
experience with automatic evaluation approaches for agentic workflow. The role
includes the opportunity to partner with world class system engineers to
prototype and incorporate bleeding edge algorithmic innovations in the context
of emerging agentic experiences. Ability to interface with large scale modeling
& data infrastructure is desired.

Requirements

• PhD, or MSc in Computer Science/Electrical Engineering, or a related field (mathematics, physics or computer engineering); with a focus on machine learning, or comparable professional experience
• Strong ML and Generative Modeling fundamentals
• Strong expertise in one of the following: Reinforcement Learning, Multimodal Training, Pre-training / Post-training foundation models
• Proficiency in using ML toolkits, e.g., PyTorch
• Proven track record of research contributions demonstrated through publications in top-tier conferences, or open source contributions to algorithm

Nice To Haves

• Experience with building & deploying Multimodal-LLMs
• Familiarity with distributed training and large-scale data infrastructure

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees