Machine Learning Engineer, Trust and Store Integrity Science

Amazon • Seattle, WA

9d

About The Position

The Selling Partner Trust and Store Integrity's (TSI) vision is that bad actors
cannot operate in our store while selling partners start and grow their business
without fear of disruption, such that customers and selling partners across the
globe trust us, and have confidence in the integrity of Amazon’s store. The
mission of the TSI Science team is to accelerate and optimize business impact
for all TSI programs through innovative, scalable, and impactful science
solutions. This includes building efficient capabilities for detecting emerging
and ongoing threats from bad actors, driving operational efficiencies through
automation, and supporting compliance initiatives in a flexible and scalable
manner. We are seeking an experienced Software Development Engineer/Machine
Learning Engineer to shape the future of our centralized science infrastructure.
This role will bridge the gap between scientific innovation and engineering
excellence, creating scalable solutions that empower multiple science teams
across the organization.

Requirements

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

Nice To Haves

• 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
• Bachelor's degree in computer science or equivalent

Responsibilities

• Lead the architectural design and implementation of enterprise-scale machine learning infrastructure and platforms that serve multiple science domains
• Drive standardization and consolidation of scientific workflows, reducing redundancy and improving operational efficiency
• Partner with science teams to understand domain-specific challenges and translate them into reusable technical solutions
• Define and implement engineering best practices for ML operations, including model deployment, monitoring, and maintenance
• Influence and guide science teams in adopting engineering rigor while maintaining their ability to innovate
• Establish clear boundaries and interfaces between MLE and Science teams, optimizing for team efficiency and delivery speed
• Lead cross-functional initiatives to build common infrastructure that scales scientific solutions into production-grade ML products
• Mentor junior engineers and scientists in software engineering best practices and ML system design

Benefits

• health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage)
• 401(k) matching
• paid time off
• parental leave

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level