Description
As an engineer in this role, you will be primarily focused on developing and using APIs in coremltools to enable ML engineers to efficiently author/convert ML models to CoreML. You will be integrating coremltools into internal and external ML model repositories to evaluate and demonstrate how ML models can ingested into CoreML. You will ideate, design, and stress test the gamut of optimizations required to ingest these models, ranging from source level optimizations (e.g., in the PyTorch program), to custom optimizations after converting to CoreML’s model representation. The role requires a good understanding of ML modeling (architectures, training vs inference trade-offs, etc.), ML deployment optimizations (e.g., quantization), and a good understanding of designing Python APIs
We are building the first end-to-end developer experience for ML development that, by taking advantage of Apple’s vertical integration, allows developers to iterate on model authoring, optimization, transformation, execution, debugging, profiling and analysis. The coremltools authoring and conversion APIs are the entrypoint to the rest of the infrastructure stack.