Machine Learning Systems Engineer, Siri Agent Modeling

**Weekly Hours:** 40 **Role Number:** 200620661-0836 **Summary** Join the team redefining what a deeply personal and integrated assistant can be. As part of the Siri organization, you will help shape one of the world's 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 visionOS. This 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. **Description** 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 Apple's 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 Apple's customers and enrich their lives. You are an ideal candidate if you: Are not afraid of CUDA OOM or NCCL errors **Minimum Qualifications** + Experience in model lifecycle of training, evaluation, and deployment of models + Strong understanding of Machine Learning (ML) model architectures (e.g. Transformers, CNN) and ML training loop + Strong proficiency in Python and ML framework such as PyTorch + Bachelor's degree in Computer Science, Engineering, or related discipline, or equivalent industry/project experience + Experience with agentic AI-assisted coding **Preferred Qualifications** + Collaborative with experience working in large inter-teams projects + Expertise in ML and LLM optimization such as quantization, KV Cache, Speculative Decoding + Familiarity with ML training methodologies such as FSDP, DDP, and other parallelism + Experience in an LLM training/eval library such as HuggingFace transformers, lm evaluation harness, Megatron-LM. + Experience in optimizing LLM models and deploying LLM models + Proficiency in a compiled programming language (e.g. Swift, C/C++, Java)