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About This Role
DESCRIPTION
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At AWS Applied AI Solutions, we're revolutionising the future of work and enterprise technology. Our vision is to empower every business to innovate with AI — making intelligent operations effortless and unleashing companies and their employees to focus on what matters most. We blend vision with curiosity and Amazon's real\-world experience to build opinionated, turnkey solutions. Where customers prefer to buy over build, we become their trusted partner with solutions that are no\-brainers to adopt and easy to use.
Our organization brings together a unique portfolio spanning multi\-modal communications, IoT, geospatial intelligence, gaming, rendering, industrial solutions, and workforce management — creating an ecosystem that helps businesses transform their ideas into reality. We stand at the forefront of a paradigm shift: traditional enterprise software is iteratively evolving into intelligent, agentic AI workspaces. By combining Amazon's deep expertise across these domains with our commitment to Agentic AI enablement, we're building the technological foundation that will shape the future of AI\-powered solutions — while maintaining the robust, scalable services that millions of customers rely on today.
The Opportunity:
This is a rare chance to define and build the foundational AI infrastructure that powers the next generation of agentic and ML\-driven applications at global scale. You will shape the architectural backbone that enables enterprises to seamlessly combine human and AI capabilities — driving large\-scale systems for workflow orchestration, real\-time decisioning, and autonomous operations across a wide range of industries.
You'll influence how businesses move beyond traditional software into fully integrated, intelligent operational environments. The systems and frameworks you architect will be the foundation that millions of businesses ultimately rely on as Amazon expands its applied AI capabilities. It's a high\-impact, high\-visibility role with real ownership — and the opportunity to leave a lasting mark on the future of enterprise AI.
What You'll Do:
Technical Leadership
- Architect and drive the technical vision for next\-generation AI infrastructure and platforms that enable Agentic AI capabilities across our service portfolio
- Design the foundational systems, frameworks, and intelligent agents that make human\-AI collaboration intuitive, trustworthy, and productive
- Build highly reliable, secure, and scalable distributed systems that seamlessly scale from small businesses to global enterprises
- Define technical standards and best practices for integrating AI capabilities across diverse domains — from multi\-modal communications and geospatial intelligence to IoT and autonomous operations
- Create extensible architectures that support expansion into new business applications, establishing reusable patterns that teams across Amazon can build upon
- Make difficult trade\-off decisions and drive awareness of the downstream impact of technical choices
- Align multiple teams to a coherent technical vision and deliver systems that fit well together
Key job responsibilities
Innovation \& Building:
- Lead the design of enterprise\-grade systems that solve complex distributed systems challenges at scale — from prototypes and demos through to production
- Architect systems that integrate human and AI workforces for next\-generation business operations, including workflow orchestration and real\-time decisioning
- Design solutions for high\-volume workflow optimisation and process automation
- Develop APIs and integration patterns for seamless interoperability across AI\-powered applications
- Drive technical innovation and establish reusable patterns and frameworks that accelerate teams across the organisation
Organizational Impact:
- Drive a strong engineering excellence and operational culture across all products and teams
- Mentor and guide senior and principal engineers while solving complex technical challenges hands\-on
- Scout and recruit top technical talent, actively growing the engineering community
- Work cross\-functionally with Principal Engineers and teams across AWS Applied AI Solutions
- Engage with key customers in executive briefings, communicating an inspiring technical vision
BASIC QUALIFICATIONS
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- Bachelor's degree in Computer Science, Computer Engineering, or equivalent
- 10\+ years of experience leading large\-scale projects in software development, infrastructure, architecture, compute, or networking
- 8\+ years of design, architecture, implementation, or consulting experience with distributed applications running on cloud
- Technical expertise in designing, architecting, and building complex, large\-scale distributed systems
- Experience improving software development lifecycle processes, best practices, operations, and automation
- Experience mentoring and growing engineering communities on complex technical challenges
- Solid coding practices including peer code reviews, unit testing, and agile development
- Excellent written and verbal communication skills with the ability to present complex technical information clearly to diverse audiences
PREFERRED QUALIFICATIONS
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- Master's or PhD in Computer Science or Computer Engineering
- 15\+ years of relevant experience
- 5\+ years of design, architecture, implementation, or consulting experience with distributed applications on AWS
- 3\+ years working directly with enterprise customers to assess needs, identify solutions, and resolve technical disagreements
- Experience with big data systems, analytics, containerised microservices, serverless functions, and event\-driven architecture
- Experience shipping business\-grade AI solutions and agentic systems
- Hands\-on experience building and iterating on AI/ML\-driven products, including prototypes through production systems
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, BELLEVUE \- 200,100\.00 \- 270,600\.00 USD annually
USA, WA, Bellevue \- 200,100\.00 \- 270,600\.00 USD annually
USA, WA, SEATTLE \- 200,100\.00 \- 270,600\.00 USD annually
USA, WA, Seattle \- 200,100\.00 \- 270,600\.00 USD annually
Salary Context
This $200K-$270K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Amazon.com, 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 $185,000 based on 13,200 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($235K) sits 27% above the category median. Disclosed range: $200K to $270K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Amazon.com AI Hiring
Amazon.com has 114 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, AI Agent Developer, Data Scientist. Positions span New York, NY, US, Seattle, WA, US, Reading, MA, US. Compensation range: $129K - $300K.
Location Context
AI roles in Seattle pay a median of $227,400 across 1,128 tracked positions. That's 13% 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (106) are outnumbered by mid-level (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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