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About This Role
DESCRIPTION
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
Salary Context
This $143K-$194K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Amazon Web Services, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($169K) sits 20% below the category median. Disclosed range: $143K to $194K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Amazon Web Services AI Hiring
Amazon Web Services has 71 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Engineer, Data Scientist. Positions span New York, NY, US, Bellevue, WA, US, Denver, CO, US. Compensation range: $97K - $299K.
Location Context
AI roles in Seattle pay a median of $226,100 across 973 tracked positions. That's 6% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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