Software Development Engineer, AWS Marketing, Data Science & Engineering (D:SE)

Within AWS Marketing, the Data Science and Engineering (D:SE) team is the central data, science, and intelligence organization. We own the data foundation, the measurement and causal economics frameworks, the scoring and intelligence models, and the agentic AI systems that power marketing decisions across AWS.

We exist because marketing at AWS scale requires more than dashboards and reports. It requires a unified data layer rich enough to train models on, composable enough for AI agents to reason over, and rigorous enough to hold investment decisions accountable. We build that layer and the intelligence products on top of it.

We're looking for a Software Development Engineer to help us build and scale our next-generation MLOps and agentic AI systems. You'll work with a serverless, AWS-native stack:

- Agentic AI - AWS Bedrock and Bedrock AgentCore (runtime, memory, tool gateway), Bedrock Guardrails, ReAct-style agent loops with model routing, and Model Context Protocol (MCP) servers that expose our data and retrieval tools to agents

- ML platform - AWS SageMaker for training pipelines, Feature Store, and real-time inference endpoints; embedding pipelines; hybrid retrieval combining dense vector similarity and BM25; reranking; and LLM-as-judge evaluation harnesses

- Compute and orchestration - AWS Lambda, AWS Step Functions, AWS EventBridge, and Amazon Managed Workflows for Apache Airflow (MWAA)

- Data and serving - AWS Redshift (via the Redshift Data API), AWS S3, AWS DynamoDB, and AWS OpenSearch Service

- Infrastructure and operations - AWS CDK for all infrastructure, so every environment is reproducible and code-reviewed rather than clicked into a console; AWS CloudWatch metrics, alarms, and dashboards; CloudWatch RUM; SNS alerting; SQS dead-letter queues; and canary deployments promoted through beta, gamma, and production

Key job responsibilities

- Design, build, and operate production agentic AI systems - agent runtimes, MCP tool servers, retrieval pipelines, guardrails, and the evaluation harnesses that keep them honest

- Own the retrieval and ranking quality loop end to end: hybrid semantic and keyword retrieval, graded relevance signals, reranking on live in-session data, and offline plus LLM-as-judge evaluation before parameters become contract

- Productionize machine learning models with your applied science partners - training pipelines, feature engineering into SageMaker Feature Store, real-time inference behind low-latency APIs, and the deployment gates that make model rollout safe

- Build and scale serverless data pipelines over datasets in the billions of rows, including ingestion connectors, transformation orchestration, and validated migrations with parallel-run and rollback strategies

- Raise the operational bar on what you own: instrumentation, actionable alarms tuned against real SLOs, data-quality and freshness observability, runbooks, load and game-day testing, and participation in an on-call rotation

- Write the design documents, drive the code reviews, and make the tradeoff calls - you'll own systems, not tickets

- Work AI-natively. Our team uses agentic development tooling daily, and we expect you to extend it as well as use it

A day in the life

You might start by triaging an alarm on an agent's tool-call latency, then pair with an applied scientist on why a reranking signal isn't lifting conversion, then review a teammate's CDK change that adds a freshness alarm to an indexing pipeline. Afternoons tend toward deeper work: a design doc for replacing a mocked data source with a live pipeline, or an evaluation run that decides whether a model change ships. You'll spend meaningful time with product and data engineering partners, because most of our interesting problems are ambiguous before they're technical.

About the team

You'll join a tight, high-impact team of software engineers, ML engineers, data engineers, applied scientists, and product managers solving problems at the intersection of marketing analytics, data science enablement, and platform engineering. We're small enough that your work is visible and unambiguously yours, and we ship fast - recent agentic AI products have gone from concept to production in a handful of sprints. You'll experience a culture that values ownership, cross-functional collaboration, and data-driven decision making.

Basic qualifications

- 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

- Bachelor's degree or foreign equivalent in Computer Science, Engineering, Mathematics, or a related field

- Experience programming with at least one software programming language

Preferred qualifications

- 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

- Experience with designing and building application using AWS services such as Lambda, AWS Elastic Beanstalk, Kubernetes

- Experience with training and deploying machine learning systems to solve large-scale optimizations, or experience in software development

- Experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware, or experience programming with at least one modern language such as Java, C++, or C# including object-oriented design

- Experience with SQL and database technologies such as AWS Redshift and expertise in performance tuning and scaling in a DWH environment

- Experience in operations and on-call support for data center facilities, mission critical plants, or production facilities, or experience leading technical teams through daily operations and maintenance evolutions

- Experience demonstrating software engineering skills in a previous intership, work experience, coding competitions, or publications, or experience with automation and any version control tools and experience that includes strong analytical skills, attention to detail, and effective communication abilities

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 - 143,700.00 - 194,400.00 USD annually