Software Development Engineer, Measurement, Ad Tech, and Data Science

- 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
- 1+ years of software development engineer or related occupational experience
- 1+ years of designing and developing large-scale, multi-tiered, multi-threaded, embedded or distributed software applications, tools, systems, and services using: C#, C++, Java, or Perl experience
- 1+ years of Object Oriented Design experience
- Experience programming with at least one software programming language
- 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

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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, CO, Boulder - 143,700.00 - 194,400.00 USD annually
Application deadline: Aug 10, 2026

Turn billions of advertising signals into real-world outcomes — building the distributed systems that power ML model execution, audience activation, and campaign optimization for the world's largest brands on Amazon Marketing Cloud (AMC). You'll own services that process millions of audience memberships daily and orchestrate customer ML workloads with strict reliability, privacy, and latency requirements.

The AMC Custom Models and Signals team owns two high-impact domains: our Custom Models platform (ML orchestration via AWS Clean Rooms, SageMaker, and Spark) and our audience and custom signals publication pipeline (high-throughput identity resolution and segment delivery to Amazon DSP and Sponsored Ads). You'll work directly with large enterprise advertisers and see your work drive measurable advertising outcomes. We're looking for an engineer who writes production-quality code, cares about operational excellence, leverages AI efficiently, and wants to solve hard distributed systems problems at scale.

Key job responsibilities
- Design, build, and operate distributed services (Java, Lambda, Step Functions, DynamoDB, Spark) that orchestrate ML model training, inference, and audience activation
- Own end-to-end delivery of features across the Custom Models platform and audience/signals publishing pipeline — from API design through deployment and production monitoring
- Investigate and resolve complex production issues spanning multiple services and AWS dependencies (Step Functions, Clean Rooms, SageMaker, Glue, S3)
- Improve system reliability, observability, and operational tooling for multiple Ads Tier 1 services
- Collaborate with science, product, and partner teams to translate customer requirements into scalable technical solutions
- Participate in on-call rotation and drive operational excellence through COEs, runbooks, and automated monitoring

About the team
AMC Custom Models and Signals is a Boulder-based team building the systems that turn AMC analytics into real-world advertising outcomes. We own Custom Models (ML orchestration), Igno (audience publishing), AMS (identity resolution and delivery), and Custom Signals API (new activation signals). Our work directly powers audience targeting, measurement, and ML-driven optimization for enterprise advertisers. We value ownership, operational rigor, and solving hard problems simply — and we're investing heavily in GenAI and agentic approaches to platform reliability and customer experience.