Sr. Software Dev Engineer (SageMaker AI)

TL;DR

Sr. Software Dev Engineer (SageMaker AI): Building AI-powered data preparation and evaluation systems for high-quality machine learning data with an accent on auto-labeling, LLM-as-judge workflows, and human-in-the-loop verification. Focus on designing distributed ML infrastructure, establishing model and agent quality standards, and continuously improving data quality at scale.

Location: USA, Washington, Seattle

Salary: $168,100–$227,400 USD annually, plus sign-on payments, restricted stock units, and benefits.

Company

AWS provides a comprehensive cloud platform used by startups and Global 500 companies to run technology products and services.

What you will do

• Design and deliver core data preparation components for customizing and fine-tuning LLMs in SageMaker.
• Build scalable auto-labeling and LLM-as-judge systems that detect, diagnose, and remediate data quality issues.
• Establish quality standards and evaluation frameworks for AI agents and models.
• Evolve human-in-the-loop services for labeling, annotation quality assurance, and ground-truth generation.
• Make architectural decisions across distributed systems, data processing, and ML infrastructure while owning the technical roadmap.
• Mentor engineers, lead design reviews, and influence technical direction across the organization.

Requirements

• 5+ years of non-internship professional software development experience.
• 5+ years of programming experience with at least one programming language.
• 5+ years of leading the design or architecture of reliable and scalable systems.
• Experience as a mentor, technical lead, or engineering team lead.
• Experience with the full software development life cycle, including coding standards, code reviews, source control, testing, and operations.
• Bachelor’s degree in computer science or equivalent experience.

Nice to have

• Experience building ML pipelines, data processing systems, or evaluation infrastructure at scale.
• Hands-on experience with LLM prompting, fine-tuning, structured output, and confidence calibration.

Culture & Benefits

• Knowledge-sharing, mentorship, and thorough code reviews support engineering growth.
• Employee-led affinity groups and inclusion-focused learning events foster a diverse workplace.
• Flexible working practices support work-life harmony.
• Benefits include health insurance, 401(k) matching, paid time off, parental leave, flexible spending accounts, and adoption and surrogacy reimbursement.
• Compensation includes sign-on payments and restricted stock units.