HomeKit Machine Learning Engineer

The HomeKit team provides the foundation which enables an entire ecosystem of secure and intelligent connected home devices. Our mission is to create a scalable, distributed system that will transform how people interact with their home accessories. We are looking for a dedicated and passionate engineer to help advance our Home platform intelligence and elevate it to new heights. As a Machine Learning Engineer you will have an opportunity to be part of Smart Home focused ML innovation within Apple, initially focused on Applied ML for Computer Vision. The team is well positioned for strategic contributions in the short-term (on well-known Apple products) and in the long-term (on highly ambitious, high-risk, high-reward projects). This role has a strong focus on shipping ML-based features and products. You’ll innovate in the entire end-to-end ML production pipeline. This includes but is not limited to; crafting creative approaches to datasets, model training, and on-device inference optimizations. Our ideal team member is fearless when it comes to trying new things and is willing to iterate on ideas. We value team members who can quickly prototype, iterating all the way to high-quality implementations., As a member of this team, you will use your background to: * Develop features and models to improve the capabilities of systems that use machine learning * Scale up model training, build data pipelines, and tuning to improve system performance * Review and implement pioneering machine learning algorithms * Build software that improves rate of experimentation

• Bachelor’s, Master’s, or PhD or equivalent experience in Computer Science or a related field.
• A strong curiosity, willingness to dive deep into unfamiliar problems, and an eagerness to learn and grow in a fast-evolving field.
• Proficient in Python and deep learning frameworks like PyTorch, as well as familiarity with a language like Swift, C, C++ or Objective C.

Preferred Qualifications

• Experience with training ML models including deep learning based models and model optimization.
• Able to define metrics, evaluate ML models, and perform error analysis.
• Familiar with recent advances in deep learning.