Apple's System Intelligent and Machine Learning (SIML) group needs to build infrastructure to support product-focused machine learning projects, leveraging computer vision and machine learning to transform how millions of people collect, discover, and share life's moments. This involves indexing terabytes of data for various ML domains and analyzing algorithm failure modes.
Requirements
- 5+ years of experience in full-stack development.
- Experience in Java and/or Python, and C/C++.
- Solid working knowledge of machine learning fundamentals.
- Proven experience in systems engineering fundamentals and system design, and can show strong expertise in one or more systems engineering stacks.
- Domain experience in machine learning, AI, computer vision, or a related field (computer graphics, image processing, multimedia, etc.).
- Experience with macOS and/or iOS development and familiarity with UNIX-based systems.
- Familiarity with Objective-C.
Responsibilities
- building infrastructure to support product-focused machine learning projects
- build systems that leverage machine learning to index terabytes of data for projects in domains like image generation, LLMs, computer vision, natural language processing, human-computer interaction and text recognition
- define and build out systems for analysis of failure modes of algorithms built upon this data
- define and build out systems for reporting overall benchmarking results for model comparisons
- play a major role in defining important datasets
- play a major role in ingestion of annotated/inferred data into our systems
- making the data available to machine learning scientists in a seamless manner
Other
- Bachelors or Master’s degree, or equivalent experience
- enjoy variety, and are passionate about working on new things on a frequent basis.
- love working with a large number of people across teams throughout the company, and driving solutions that take multiple partners into account.
- either a machine learning industry pro, or an outstanding engineer wanting to expand your horizons and learn more about the nitty gritty of how ML projects are launched from inception to release.
- Ability to provide publicly-available samples of work done, or shown success in coding competitions/hackathons is desired.
