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 Research Engineer 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/

DESCRIPTION

Important attributes expected in the role is fluency in algorithm development
(prompt optimization, post training / alignment), and experience with automatic
evaluation techniques. 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 Other
responsibilities include testing and upkeep of training infrastructure, whiled
partnering with safety/security teams on emerging robustness challenges while
aligning models/agents to production needs. Ability to interface with large
scale data infrastructure is a huge plus. Apple has a thriving Machine Learning
research community. It is expected that role offers the candidate an opportunity
to form a strong network of collaborators across the company, while sharing
research progress with senior technical leaders at a regular cadence.

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
• Proven experience in one of the following: Reinforcement Learning, Multimodal Training, Pre-training / Post-training foundation models
• Proficiency in using ML toolkits, e.g., PyTorch
• 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

Responsibilities

• algorithm development (prompt optimization, post training / alignment)
• automatic evaluation techniques
• testing and upkeep of training infrastructure
• partnering with safety/security teams on emerging robustness challenges while aligning models/agents to production needs
• Ability to interface with large scale data infrastructure

What This Job Offers

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