Machine Learning Engineer

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team, you'll have the opportunity to be part of a leading ML innovation organization that develops a wide range of impactful Apple features. Within MIND, you'll engage in cutting-edge research in fields such as Foundation Models and Perception, and collaborate to create end products that have high impact on Apple users. This role places a strong emphasis on shipping ML-based features and products. You'll be involved in the entire end-to-end ML development pipeline, which encompasses creative approaches to dataset curation, model training, runtime inference integration, and on-device model optimizations. Our ideal team member is fearless in exploring new ideas and is willing to iterate on concepts. We value team members who can swiftly prototype and iterate, ultimately resulting in high-quality implementations. Our team seeks a self-driven machine learning engineer with strong experience in building ML training pipelines and developing production-quality inference infrastructure. In this role, you are expected to collaborate closely with ML researchers, SW/FW engineers, and Operation/Data engineers to advance different research and production efforts. Your role is to help deliver the needed pipeline for model development and evaluation, as well as build the production software to integrate these models and related functionality within Apple's software infrastructure. In addition, as part of the development process you are expected to build real-time demos and visualizations of sensing data streams and model predictions. Build ML models and end-to-end training and evaluation pipeline. Develop ML production software. The ability to build end-to-end demos for ML solutions. Represent the team in Cross-functional discussions. A PhD in computer science, computer engineering, or relevant Fields. Alternately, a BS or an MS + 3 to 5 years of ML engineering experience also qualifies. Strong foundation in machine learning, and more specifically in LLM and multimodal foundation models. Experience in building model training/eval pipelines in Python/PyTorch. Experience in prototyping and developing software applications (preferably in Swift). Experience with sensors and sensing systems. Strong communication and presentation skills.

• Ability to work in a collaborative environment.Proficient programming skills in Python and one of the deep learning toolkits such as JAX, PyTorch, or TensorFlow. Experience in on-device ML model deployment and on-device optimization. Experience handling multimodal data including text, images, audio, and other sensors. Experience in developing production software. Proficient in Swift, Objective-C, C++, or Go