Sr ML Researcher Multi-Modal Reasoning

Summary Do you believe generative models can transform creative workflows and smart assistants used by billions? Do you believe it can fundamentally shift how people interact with devices and communicate, personalizing and tailoring experiences to their unique needs? SIML’s Content Understanding teams strives to turn cutting edge research into compelling user experiences that realize all these goals and more, working on Apple Intelligence technologies such as Image Playground, Genmoji, Generative Memories, Semantic Search, and many more. 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. Description We are looking for a senior technical leader experienced in architecting and deploying production scale multimodal ML. An ideal candidate has the ability to lead diverse cross functional efforts ranging from ML modeling, prototyping, validation and private learning. Solid ML fundamentals and an ability to place research contributions with respect to state of the art would be an essential part of the role. Experience with training and adapting large language models would be an important need. This 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 a huge plus. Preferred Qualifications Experience with building & deploying Multimodal-LLMs. Familiarity with distributed training and large-scale data infrastructure. Minimum Qualifications PhD with 5+ years experience or MS with equivalent experience, in Computer Science/Electrical Engineering, or a related field (mathematics, physics or computer engineering); with a focus on machine learning, or comparable professional experience. Hands on experience training LLMs/adapting pre-trained LLMs for downstream tasks & alignment. Modeling experience at the intersection of NLP and Vision. Proficiency in ML toolkit of choice, e.g., PyTorch. Strong programming skills in Python. Proven track record of research contributions demonstrated through publications in top-tier conferences, or open source contributions to algorithm. #J-18808-Ljbffr Apple