Interested in this AI/ML Engineer role at Amazon.com?
Apply Now →Skills & Technologies
About This Role
DESCRIPTION
---------------
In September 2019, Amazon co\-founded The Climate Pledge, a commitment to reach net zero carbon by 2040, ten years ahead of the Paris Agreement. Achieving that goal for hundreds of millions of devices requires more than good intentions — it requires embedding carbon intelligence into the way products are designed, measured, certified, and communicated to customers. That’s what SUSTAIN.AI builds.
We are the AI and automation team within Amazon’s Devices \& Services Sustainability organization. Our mission is to turn sustainability from a manual, expert\-intensive process into a scalable, intelligent capability that reaches every product team and every device. We build the platforms and data infrastructure that sustainability teams depend on daily. We build AI systems that automate environmental measurement and certification workflows. And we’re building toward a future where an engineer choosing between materials during design gets instant visibility into the carbon, cost, and supply chain implications of each option — without learning a new tool or changing how they work.
This isn’t a team that talks about AI — it’s a team that ships it. We build ML estimation pipelines, LLM\-powered data extraction tools, and end\-to\-end automation that connects supply chain data to certification\-ready environmental assessments. We also design and operate cloud\-native services on AWS — the same serverless architectures, APIs, and infrastructure\-as\-code that power Amazon’s core businesses. We prototype fast, validate against real devices, and put working systems in front of scientists and program managers who depend on them daily. The work spans from training models and building LLM\-powered tools to shipping production services, data pipelines, and keeping them running.
As a Software Development Engineer on SUSTAIN.AI, you’ll join a small, high\-ownership team of experienced engineers working at the intersection of AI, data engineering, and environmental science. You’ll partner with sustainability scientists to define the methodology, product and program managers to shape requirements and run certification workflows, and third\-party assurers to validate the results. You’ll learn from seasoned SDEs with deep expertise across ML and distributed systems — and your code will directly shape how Amazon scales its sustainability commitments across every device it makes.
You’ll have the opportunity to:
- Build AI systems that fundamentally change how environmental impact is measured and certified at scale
- Work across the full ML lifecycle — from rapid prototyping to production deployment and validation against real\-world ground truth
- Develop LLM\-powered tools that extract structured knowledge from unstructured supply chain data
- Build data pipelines that connect internal platforms and external partners into automated certification workflows
- Contribute to an approach that could set the standard for AI\-informed sustainability assessment across the industry
- Work in a small team where your decisions shape the technical direction and your code ships to production
We’re looking for builders who are excited about complex technical challenges, comfortable navigating ambiguity across multiple teams, and want to grow their careers where environmental science meets AI.
Key job responsibilities
Software Development \& AI Systems
- Design and implement estimation pipelines that combine rule\-based classifiers, ML models, and LLM\-based extraction
- Build and operate cloud\-native services on AWS — serverless architectures, APIs, CDK infrastructure, CI/CD pipelines
- Write clean, well\-tested code that operates reliably in production
- Develop and maintain data pipelines that ingest and transform supply chain data from multiple sources
Rapid Prototyping \& Validation
- Build rapid prototypes to test estimation approaches — validate with real devices before committing to production
- Design and run validation experiments comparing AI\-estimated results against manual ground truth
- Balance speed of experimentation with production readiness
- Evaluate emerging AI techniques (agentic workflows, structured extraction) through hands\-on experimentation
Cross\-Team Collaboration
- Work with sustainability scientists to define estimation methodologies and quality evaluation criteria
- Partner with program managers who run certification workflows — your tools directly reduce their manual workload
- Support third\-party certification engagements by implementing data quality and provenance requirements
- Translate scientific requirements into working software
Operational Excellence
- Monitor and maintain production services and data pipelines that support active certification workflows
- Implement logging, metrics, and provenance tracking for auditability
- Participate in on\-call rotation to ensure service reliability
- Debug and resolve production issues
Ownership \& Growth
- Take end\-to\-end ownership of features from prototype through production deployment and validation
- Contribute to architectural decisions in a team where every engineer’s voice shapes the system design
- Stay current with advances in AI, ML, and sustainability technologies
- Share knowledge through documentation, design reviews, and team presentations
A day in the life
A typical day involves pairing with sustainability scientists to translate estimation methodologies into working code, building and testing ML pipelines, participating in design reviews with the team, and deploying to production. You’ll regularly work with program managers running device certifications, product teams exploring material trade\-offs, and external assurers validating our methodology. The mix of science collaboration, hands\-on engineering, and cross\-team problem\-solving means no two weeks look the same. We protect focus time (no meetings Tuesday mornings and Friday afternoons), support each other through open collaboration, and operate with a high degree of trust — you’ll have real ownership over your work and the flexibility to do it well.
About the team
Amazon Devices \& Services Sustainability is building the future of sustainable product development at scale. Sustain.AI Sustainability Technology Amplifying Innovation—is our AI and automation team that enables sustainability experts to focus on what matters most: reducing Amazon Devices' carbon footprint. We build intelligent systems that transform manual sustainability workflows into automated, data\-driven processes, enabling our organization to scale our environmental impact without scaling overhead.
Our mission is to eliminate operational bottlenecks through automation and AI, creating the technical foundation that enables breakthrough sustainability innovations essential for net zero.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
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, VA, Arlington \- 143,700\.00 \- 194,400\.00 USD annually
Salary Context
This $143K-$194K range is above the median for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. Disclosed range: $143K to $194K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Amazon.com AI Hiring
Amazon.com has 488 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Data Scientist, Research Scientist. Positions span New York, NY, US, Seattle, WA, US, Arlington, VA, US. Compensation range: $52K - $342K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.