Amazon Lab126 is an inventive research and development company that designs and engineers high-profile consumer electronics. Lab126 began in 2004 as a subsidiary of Amazon.com, Inc., originally creating the best-selling Kindle family of products. Since then, we have produced groundbreaking devices like Fire tablets, Fire TV, and Amazon Echo. What will you help us create?
Are you an experienced Machine Learning researcher who is capable of diving deep into hard technical problems and coming up with insightful solutions that enable successful products? Are you unafraid of tackling a mind-bending challenge that really will improve peoples' lives in a meaningful way? Are you a finisher who can deliver robust, production-quality code that solves complex, real-world problems in ways that delight customers?
If this describes you, come join our team at Lab126 in the heart of Silicon Valley. The team is using machine learning, robotics, and real-time and distributed systems to convert requirements into concrete deliverables. A Researcher on this team will translate business and functional requirements into working code. Comfort with a high degree of ambiguity and ability to solve problems that haven’t been solved to scale before are essential.
Key responsibilities will be to conduct research and development in machine learning, and to collaborate with cross-functional engineering teams to put the concepts you develop into production. You will determine where commercially available solution and academic research can be applied to solve Amazon business problems, as well as identify opportunities for innovation. You will use a large amount of data to train and test algorithms to bring them up to production level quality. In this role, you will:
· Research, design, implement and evaluate planning and algorithms
· Work on large-scale datasets, focusing on creating scalable and accurate machine learning systems in versatile application fields
· Collaborate closely with team members on developing systems from prototyping to production level
· Collaborate with teams spread all over the world
· Work closely with software engineering teams to drive scalable, real-time implementations
· Track general business activity and provide clear, compelling management reports on a regular basis