Description
Join us in building the future of AI-powered data preparation with SageMaker, where we're revolutionizing how organizations ensure data quality for their machine learning initiatives. As part of a strategic initiative to create next-generation data quality and evaluation systems, you'll work at the intersection of latest AI and foundational ML infrastructure.
The AI data labeling market is exploding — $3.2B in 2026, projected $25B by 2028 — and the bottleneck to better AI is no longer compute, it's high-quality labeled data at scale. We're building the platform that solves this: auto-labeling with statistical quality guarantees, LLM-as-judge evaluation, and human verification — all unified under one managed service.
This is an opportunity to be part of a team launching innovative AI-powered data preparation products from the ground up. You'll architect systems that produce training data at human quality and machine scale — where LLMs label, humans verify, and the system continuously improves from every correction. The role offers high visibility with AWS leadership and the chance to shape products that will transform how businesses prepare and govern their ML data.
We're seeking Sr. SDE who thrives in a fast-paced, collaborative environment and isn't afraid to tackle seemingly impossible challenges. You'll build rock-solid, highly-secure software at world-class scale that combines auto-labeling, human-in-the-loop workflows, and LLM-as-Judge techniques to deliver data quality improvements—while partnering closely with ML science teams to push the boundaries of what's possible.
Key job responsibilities
1. Data Preparation Platform: Design and deliver core components of data preparation journey to customize and fine-tune LLMs in SageMaker, designing systems that provide customers with high-quality, reliable data for their ML workflows.
2. Drive Innovation in Data Preparation: Build and scale systems leveraging auto-labeling and LLM-as-Judge techniques to automatically detect, diagnose, and remediate data quality issues.
3. Agent & Model Quality: Establish quality standards and evaluation frameworks for AI agents and models, implementing continuous improvement processes.
4. Human-in-the-Loop Services: Lead the evolution of our HITL suite, enabling seamless human feedback loops for data labeling, annotation quality assurance, and ground truth generation.
5. Technical Leadership: Mentor engineers, drive design reviews, and raise the engineering quality bar across the team. Influence technical direction without formal authority.
6. Architecture & Strategy: Make high-judgment architectural decisions across distributed systems, data processing, and ML infrastructure. Own the technical roadmap for your area.
About the team
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future.
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
About AWS
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
The AI data labeling market is exploding — $3.2B in 2026, projected $25B by 2028 — and the bottleneck to better AI is no longer compute, it's high-quality labeled data at scale. We're building the platform that solves this: auto-labeling with statistical quality guarantees, LLM-as-judge evaluation, and human verification — all unified under one managed service.
This is an opportunity to be part of a team launching innovative AI-powered data preparation products from the ground up. You'll architect systems that produce training data at human quality and machine scale — where LLMs label, humans verify, and the system continuously improves from every correction. The role offers high visibility with AWS leadership and the chance to shape products that will transform how businesses prepare and govern their ML data.
We're seeking Sr. SDE who thrives in a fast-paced, collaborative environment and isn't afraid to tackle seemingly impossible challenges. You'll build rock-solid, highly-secure software at world-class scale that combines auto-labeling, human-in-the-loop workflows, and LLM-as-Judge techniques to deliver data quality improvements—while partnering closely with ML science teams to push the boundaries of what's possible.
Key job responsibilities
1. Data Preparation Platform: Design and deliver core components of data preparation journey to customize and fine-tune LLMs in SageMaker, designing systems that provide customers with high-quality, reliable data for their ML workflows.
2. Drive Innovation in Data Preparation: Build and scale systems leveraging auto-labeling and LLM-as-Judge techniques to automatically detect, diagnose, and remediate data quality issues.
3. Agent & Model Quality: Establish quality standards and evaluation frameworks for AI agents and models, implementing continuous improvement processes.
4. Human-in-the-Loop Services: Lead the evolution of our HITL suite, enabling seamless human feedback loops for data labeling, annotation quality assurance, and ground truth generation.
5. Technical Leadership: Mentor engineers, drive design reviews, and raise the engineering quality bar across the team. Influence technical direction without formal authority.
6. Architecture & Strategy: Make high-judgment architectural decisions across distributed systems, data processing, and ML infrastructure. Own the technical roadmap for your area.
About the team
Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge-sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects that help our team members develop your engineering expertise so you feel empowered to take on more complex tasks in the future.
Diverse Experiences
AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
About AWS
Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses.
Inclusive Team Culture
Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness.
Work/Life Balance
We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud.
Mentorship & Career Growth
We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.
Basic Qualifications
- 5+ years of non-internship professional software development experience
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team
- 5+ years of programming with at least one software programming language experience
- 5+ years of leading design or architecture (design patterns, reliability and scaling) of new and existing systems experience
- Experience as a mentor, tech lead or leading an engineering team
Preferred Qualifications
- 5+ 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
- Experience building ML pipelines, data processing systems, or evaluation infrastructure at scale
- Hands-on experience with LLMs (prompting, fine-tuning, structured output, confidence calibration)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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.
USA, WA, Seattle - 168,100.00 - 227,400.00 USD annually
- Bachelor's degree in computer science or equivalent
- Experience building ML pipelines, data processing systems, or evaluation infrastructure at scale
- Hands-on experience with LLMs (prompting, fine-tuning, structured output, confidence calibration)
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
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.
The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including 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, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.
USA, WA, Seattle - 168,100.00 - 227,400.00 USD annually
