Jobs
Required skills
Machine Learning
Lead the future of AI-powered compliance automation at Amazon scale. Build and scale a high-impact services that processes billions of documents annually using state-of-the-art machine learning and large language models to ensure products sold on Amazon meet global regulatory, safety, and compliance requirements.
In this role, you will lead a team building state-of-the-art ML and engineering solutions for media automation. Your team will develop advanced document processing systems that extract, validate, and enrich compliance information at unprecedented scale - enabling Amazon to verify product safety across our global marketplace.
This is an opportunity to shape the technical strategy and organizational growth of a foundational capability within Customer Trust organization. You'll partner with senior leaders across compliance, product, and engineering to drive innovation in automated document processing, mentor and develop technical leaders, and create reusable systems that reduce time-to-market while scaling compliance operations globally. Your work will directly impact customer safety and trust across Amazon's worldwide operations.
Join us in building the infrastructure that keeps Amazon customers safe.
- Key job responsibilities
- Lead multiple Software Development and Applied Scientists functions building advanced document processing systems that extract, validate, and enrich compliance information from billions of documents annually
- Drive technical vision and strategy for AI-powered media automation capabilities, including vision language models, content processing pipelines, and machine learning infrastructure that enable Amazon to scale compliance operations globally
- Partner with senior leaders across compliance, product, engineering, and business teams to define roadmaps, prioritize initiatives, and align technical solutions with regulatory requirements across multiple jurisdictions
- Build and scale high-performing engineering and science teams through hiring, mentoring, and developing technical leaders who deliver state-of-the-art ML and engineering solutions
- Own critical system performance metrics and create data-driven strategies for infrastructure growth, cost optimization, and continuous improvement in processing accuracy and efficiency
- Architect scalable, reusable processing functions that reduce time-to-market and total ownership costs while expanding adoption across Customer Trust organizations
About the team
Our team builds the machine learning and engineering infrastructure that powers compliance automation across Amazon's global marketplace. Engineers and scientists collaborate on SOTA AI technologies, including vision language models and large-scale distributed systems, that solve complex problems at the intersection of computer vision, natural language processing, and regulatory verification. We foster a culture of technical excellence where team members drive measurable impact through innovation, with opportunities to influence architecture decisions and mentor emerging technical leaders. The work environment emphasizes collaboration across disciplines, rapid experimentation, and ownership of end-to-end solutions that directly affect customer safety.
Basic Qualifications
- Experience in engineering
- Experience in engineering team management
- Experience in planning, designing, developing and delivering consumer software
- Experience partnering with product or program management teams
- Experience managing multiple concurrent programs, projects and development teams in an Agile environment
- BS degree in computer science, computer engineering, or related technical field
- Experience dealing well with ambiguity, prioritizing needs, and delivering measurable results in an agile environment
Preferred Qualifications
- Experience designing and developing large scale, high-traffic applications
- Experience in building scalable systems based on web services
- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations
- Experience solving complex problems quantitatively and develop actionable data-driven business recommendations
Amazon is an equal opportunities employer. We believe passionately that employing a diverse workforce is central to our success. We make recruiting decisions based on your experience and skills. We value your passion to discover, invent, simplify and build. Protecting your privacy and the security of your data is a longstanding top priority for Amazon. Please consult our Privacy Notice (https://www.amazon.jobs/en/privacy_page) to know more about how we collect, use and transfer the personal data of our candidates.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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About Amazon

Amazon
PublicAmazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.
10,001+
Employees
Seattle
Headquarters
$1.5T
Valuation
Reviews
2.9
10 reviews
Work-life balance
2.8
Compensation
3.7
Culture
2.5
Career
2.3
Management
2.1
35%
Recommend to a friend
Pros
Good pay and compensation
Strong benefits package
Flexible scheduling options
Cons
Poor management and leadership
Limited growth and promotion opportunities
High stress and demanding work environment
Salary Ranges
4 data points
L2
L3
L4
L5
L6
L2 · Data Analyst L2
0 reports
$108,330
total per year
Base
$43,332
Stock
$54,165
Bonus
$10,833
$75,831
$140,829
Interview experience
10 interviews
Difficulty
3.7
/ 5
Duration
21-35 weeks
Offer rate
20%
Experience
Positive 10%
Neutral 10%
Negative 80%
Interview process
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Onsite/Virtual Loop
6
Team Matching
7
Offer
Common questions
Coding/Algorithm
System Design
Behavioral/STAR
Leadership Principles
Technical Knowledge
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