Software Engineer: ML-Driven, Scalable Systems

Amazon; Seattle (WA); On-site; USD 144,000 - 194,000; Full time

Benefits offered by this job

Health insurance

RSU/stock options

Job summary

Amazon's PXTCS team uses economics, behavioral science, statistics, machine learning, and Generative AI to proactively identify mechanisms and process improvements that simultaneously improve Amazon and the lives, well-being, and value of work for Amazonians.

As a Software Development Engineer on PXTCS, you'll work side by side with economists, data scientists, data engineers, and applied scientists turning leading-edge ML and Generative AI models into reliable, scalable production systems.

Qualifications

• 3+ years of non-internship professional software development experience.
• 2+ years of non-internship design or architecture (patterns, reliability, scaling).
• Experience programming with at least one software programming language.

Responsibilities

• Work with teams across the org to deliver large distributed projects to delight our customers.
• Write high-quality, efficient, testable code in Java and other object-oriented languages.
• Build highly available, secure systems in a microservices architecture.
• Collaborate with some of the best engineers in the industry.
• Own and operate the systems that you build.
• Contribute to planning, design, implementation, testing, operations, and process improvement.
• Get firsthand experience engineering client apps as well as AWS services such as Lambda, EC2, S3, DynamoDB and others.

Skills

3+ years of software development

2+ years design/architecture

Java or other OO language

Education

Bachelor's degree in CS or equivalent

Job description

Amazon's PXTCS team uses economics, behavioral science, statistics, machine learning, and Generative AI to proactively identify mechanisms and process improvements that simultaneously improve Amazon and the lives, well-being, and value of work for Amazonians.

As a Software Development Engineer on PXTCS, you'll work side by side with economists, data scientists, data engineers, and applied scientists turning leading-edge ML and Generative AI models into reliable, scalable production systems.