Software Development Engineer, Amazon

• Experience programming with at least one modern language such as Java, C++, or C# including object-oriented design
• Experience contributing to the architecture and design (architecture, design patterns, reliability and scaling) of new and current systems
• Experience in professional, non-internship software development
• Successful candidates will have strong technical ability, self driven, excellent teamwork, and communication skills, and a motivation to achieve business results in a fast-paced environment

Desirable

• The SD bidder team is behind the brain of the Sponsored Display ad serving stack
• We deliver large impact at huge scale and are proud of the scalability, stability and operational excellence of our system, but acknowledge the sly is the limit for our ambition to improve
• You will help harmonise our product stack, enabling simple monitoring and self healing that allow us to do more with less through automation and rapid diagnostic tools
• You will work with scientists on rapid data gathering prototypes and analyses and represent the rigor of engineering in the conversation; you will engage with scientists, product and finance to align on the benefits of the proposed approach
• You will partner with our product teams, bringing your own ideas to the conversation and aligning on work, adjusting priorities based on business requirements and fast iteration on experiments
• Work side by side with our scientists to deliver code changes impacting all our ads stack
• Rapidly prototype and test many possible hypotheses/implementation alternatives in a high-ambiguity environment, making use of both quantitative analysis and business judgement
• Work with very large datasets
• Integrate successful experimental results into large-scale, highly complex Amazon production systems capable of handling 100,000s of transactions per second at low latency
• Automate, monitor and self heal our systems
• You will embody engineering rigor, designing and executing proofs of concept that validate the technical efficacy and business value of our product hypotheses
• Then bringing them into production, balancing speed of learnings with operational excellence