Research Engineer, Reasoning u0026 Memory - SIML

The System Intelligence and Machine Learning (SIML) Content Understanding teams are seeking a Research Engineer in Reasoning u0026 Memory Systems. You will be working alonside teams that are in charge of operating system wide embeddings, personalized RAG work streams, tool calling, context compaction / efficiency u0026 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) (Use the "Apply for this Job" box below). (b)  (c)

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.

Experience with building u0026 deploying Multimodal-LLMs Familiarity with distributed training and large-scale data infrastructure

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