Machine Learning Systems Engineer, Siri Agent Modeling

nJoin the team redefining what a deeply personal and integrated assistant can be. nnAs part of the Siri organization you will help shape one of the worlds most widely used AI assistants powered by our next-generation of Apple Intelligence with capabilities like personal context understanding and on-screen awareness built with privacy from the ground up. Your work will have direct meaningful impact for users across iOS iPadOS macOS watchOS and is a rare opportunity to build at the intersection of cutting-edge AI and human-centered design shipping technology that is centered around users and their needs.n

As a Machine Learning Systems Engineer you will work closely with Siri modeling teams and other cross-functional teams to optimize model training and inference. You will be working across the ML stack at Apple finding opportunities to make models performant train quicker and run faster on Apples custom Apple Silicon. You will be joining a team that spans data modeling evaluation deployment and working with engineers across ML infrastructure inference and framework teams. You will write production-level code to train and deploy models that will impact Apples customers and enrich their lives. You are an ideal candidate if you: Are not afraid of CUDA OOM or NCCL errors

Experience in model lifecycle of training evaluation and deployment of modelsnStrong understanding of Machine Learning (ML) model architectures (e.g. Transformers CNN) and ML training loopnStrong proficiency in Python and ML framework such as PyTorchnBachelors degree in Computer Science Engineering or related discipline or equivalent industry/project experiencenExperience with agentic AI-assisted coding

Collaborative with experience working in large inter-teams projectsnExpertise in ML and LLM optimization such as quantization KV Cache Speculative DecodingnFamiliarity with ML training methodologies such as FSDP DDP and other parallelismnExperience in an LLM training/eval library such as HuggingFace transformers lm evaluation harness in optimizing LLM models and deploying LLM modelsnProficiency in a compiled programming language (e.g. Swift C/C Java)