AIML - Machine Learning Engineer, MIND

As a Machine Learning Engineer in the Machine Intelligence Neural Design (MIND) team, you will have an opportunity to be part of an ML innovation organization within Apple that has its roots in the computer vision research community. 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.

Description

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

Minimum Qualifications

Proficient in Python and deep learning frameworks like PyTorch.

Experience with training ML models including deep learning based models.

Able to define metrics, evaluate ML models, and perform error analysis.

Familiar with recent advances in deep learning.

Bachelor's, Master's, or PhD or equivalent experience in Computer Science or a related field.

Preferred Qualifications

Experience with modeling vision problems in the areas of object detection, facial recognition, and/or temporal machine learning.

Experience with building efficient ML models through HW/SW co-design.

Experience crafting and defining datasets and metrics for novel tasks.

Experience with shipping ML features and products.

Excellent problem-solving and analytical skills with a thorough approach.

Most importantly: a strong curiosity, willingness to dive deep into unfamiliar problems, and an eagerness to learn and grow in a fast-evolving field.