Machine Learning Engineer, Amazon Tablets

Amazon is a global technology company that develops and sells a wide range of products and services, including e-commerce, cloud computing, digital streaming, and artificial intelligence.
Machine Learning
Mid-Level Software Engineer
In-Person
5,000+ Employees
AI · Consumer

Description For Machine Learning Engineer, Amazon Tablets

Do you want to build cutting-edge AI/ML technologies that reinvent the user experience for Amazon devices? Do you want to work alongside a team of passionate and talented scientists and engineers on the next generation of intelligent customer experiences leveraging state-of-the-art deep learning and generative models? The Amazon Tablets Core Machine Learning team seeks to hire an ML Engineer who has a solid background in the design and development of scalable AI/ML systems, experience deploying models into production to serve millions of customers, a deep passion for building AI-driven products, and a proven track record of executing complex projects and delivering high business impact.

You will be an early member of the team -- together with your team members, you will play a key role in establishing a strong foundation for customer-focused machine learning on the team. This role will provide you with the opportunity to drive impact at Amazon-scale, as well as work closely with ML scientists in a cross-functional organization.

The long-term vision of the team is to make Amazon devices effortlessly engaging for all our customers. We are creating an experience that understands why and when customers engage with different types of contents or take different actions, and build our products to foresee and satisfy those needs. We are building the capabilities to give Amazon devices the ability to help customers discover and find all types of relevant information and contents through conversational interactions, suggestions, and personalized recommendations. We are exploring ways to enable an intelligent customer experience by leveraging advanced deep learning and large language models, generative AI, and machine learning on device.

Key job responsibilities: As an ML Engineer, you will design and develop AI/ML products that involve large-scale data processing and modeling. You will be responsible to help define requirements, create software designs, implement code to these specifications, and support products while deployed and used by our customers. As an ML Engineer, you will be expected to:

  • Work with applied scientists, data scientists, engineers, and product managers to design and deliver AI/ML solutions in production at scale.
  • Develop ML workflows and end-to-end pipelines for data preparation, training, deployment, monitoring, etc., and ensure the quality of architecture and design of our ML systems and data infrastructure.
  • Maintain and continuously improve our existing ML workflows, MLOps, and infrastructure.
  • Deliver customer-facing and internal ML solutions that empower our devices to provide the best experience for our customers.
Last updated 3 months ago

Responsibilities For Machine Learning Engineer, Amazon Tablets

  • Design and develop AI/ML products involving large-scale data processing and modeling
  • Define requirements, create software designs, implement code, and support deployed products
  • Work with cross-functional teams to design and deliver AI/ML solutions in production at scale
  • Develop ML workflows and end-to-end pipelines for data preparation, training, deployment, and monitoring
  • Maintain and improve existing ML workflows, MLOps, and infrastructure
  • Deliver customer-facing and internal ML solutions for Amazon devices

Requirements For Machine Learning Engineer, Amazon Tablets

Python
  • Bachelor's degree in computer science or equivalent
  • Experience (non-internship) in professional software development
  • Experience in machine learning, data mining, information retrieval, statistics or natural language processing
  • Experience designing or architecting (design patterns, reliability and scaling) of new and existing systems

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