Cover image - Lavorare all'estero
Altro

Software Development Engineer Test | Amazon

Palo Alto • Informatica e Tecnologia
Descrizione

Amazon SageMaker Neo is an AWS service that enables machine learning models to train once and run anywhere. Neo automatically optimizes TensorFlow, MXNet, PyTorch, ONNX, and XGBoost models for deployment on ARM, Intel, and Nvidia processors. Neo uses open source projects including several machine learning compilers and a common runtime for all compiled models.

We are looking for software developers who can help us build and maintain a robust, scalable test infrastructure for our products and services.

Requisiti

QUALIFICHE DI BASE
· Bachelor's in Computer Science, Computer Engineering, or equivalent subject expertise in AI and deep learning

· Proven ability to lead software development teams through multiple releases of an enterprise software product or cloud service deployed in production

· Proven ability to design, develop, and deploy software in production as an individual contributor in an early-stage of product lifecycle

· Proven ability to influence the development trajectory of related but distinct software development efforts across multiple organizations

· Demonstrable experience developing and deploying models using at least one deep learning framework
· Must have experience with building tests or test infrastructure for large software systems

QUALIFICHE PREFERENZIALI
· Proven ability to drive feature development through multiple releases of an enterprise software product or cloud service deployed in production

· Proven ability to design, develop, and deploy software in production as an individual contributor in an early-stage of product lifecycle

· Proven ability to influence the development trajectory of related but distinct software development efforts across multiple organizations

· Demonstrable contributions to open source projects and communities through code, documentation, and technical evangelism

· Demonstrable understanding of the current features and limitations of popular deep learning frameworks

Job moments