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About This Role
DESCRIPTION
We are hiring Principal Engineers and Senior Principal Engineers to join AWS Compute and ML Services.
AWS Compute and ML Services is the compute layer the internet runs on. Millions of customers — from early-stage startups to the world's largest enterprises — depend on us every second of every day. The problems here involve a level of scale that is unique among many axes: we enable a wide variety of hardware platforms, support dozens of geographies worldwide, land a large number of new servers every day, power a vast marketplace of AI models, and meet the security and commerce needs of the most diverse customer base in cloud computing.
We don't build on top of infrastructure. We are the infrastructure.
AWS Compute and ML Services is entering its most ambitious era. AI workloads are demanding unprecedented scale, interconnect bandwidth, and placement intelligence. Confidential computing is raising the bar on what "secure by default" means at the hardware level — we're building toward a world where zero-trust isn't a feature, it's the architecture. Graviton is redefining the price-performance curve with every generation. And customers are pushing us to deliver bare-metal performance with full isolation, at global scale, with millisecond-level responsiveness.
We need engineers who can lead large teams to deliver what customers want in the AI era.
What Makes This Different:
You won't be optimizing an existing system. You'll be deciding what the system is.
The Problems — How do you architect systems that serve more types of customers, across more hardware platforms, in more regions than anyone else — and make it feel seamless? How do you co-design silicon and software when the feedback loop is measured in years, not sprints? How do you build placement algorithms that simultaneously optimize availability, performance, cost, carbon, and constraints that haven't been invented yet? How do you enable an AI model marketplace that scales from a single inference call to a 10,000-GPU training cluster? How do you make security guarantees that hold at every layer — from custom silicon to the application boundary — without sacrificing performance?
The Constraints — Microseconds matter. Billions of dollars of customer workloads depend on your decisions. There's no "move fast and break things" when you are the thing everything else is built on. You'll navigate complex tradeoffs across performance, security, and cost while maintaining the highest standards for operational excellence.
The Reward — Your work ships to every AWS customer. Not eventually. Not behind a feature flag. The abstractions you design become the foundation other services assume will always be there. You'll serve as the technical conscience for one of AWS's most critical platforms — the one everything else depends on.
Key job responsibilities
At the Principal Engineer level, you will:
Own the technical architecture and hands-on delivery of critical systems — whether that's compute platforms, ML infrastructure, serverless runtimes, security foundations, or custom silicon integration
Drive end-to-end system design decisions across the full stack, from hardware interfaces to customer-facing APIs, where latency, security, and reliability are non-negotiable
Lead cross-team initiatives spanning multiple AWS organizations to deliver cohesive systems, not disconnected components
Author and champion technical vision documents, influence product roadmaps, and represent AWS Compute and ML Services in executive-level architectural reviews
Make hard architectural calls at the hardware-software boundary where tradeoffs are permanent and impact is measured in billions of customer-hours
Mentor and develop senior engineers — raising the technical bar through design reviews, code reviews, and hands-on technical leadership
Establish and raise the operational bar: you build it, you own it, you make it better
At the Senior Principal Engineer level, you will additionally:
Set the long-term technical direction and multi-year engineering vision across AWS Compute and ML Services, influencing strategy at the VP and SVP level
Drive large-scale architectural transformations that span multiple product areas and organizations, defining technical standards that become the foundation for the broader AWS infrastructure
Serve as the technical conscience for the organization — making decisions that shape industry direction and influence how customers build on AWS for years to come
Partner with senior leadership to translate business and customer needs into durable technology investments, aligning technical strategy across compute, AI/ML, serverless, and silicon programs
Grow the Principal Engineering community — mentoring PEs, conducting promotion assessments, and shaping the culture of engineering excellence across thousands of builders
About the team
AWS Compute and ML Services sits at the intersection of the most important trends in cloud computing — AI-scale infrastructure, hardware-software co-design, confidential computing, and sustainable operations. The organization encompasses:
EC2 (Elastic Compute Cloud) — the backbone of cloud compute
AI/ML Infrastructure & Services — including SageMaker, Bedrock, and the platforms that power generative AI at scale
Serverless Compute — event-driven and container-based compute that abstracts infrastructure entirely
AWS Platform Experience — the tools and interfaces that make it all accessible to customers
This is one of the largest and most technically ambitious engineering organizations at Amazon, with thousands of engineers across multiple pillars. You'll work alongside people who've built the systems the industry takes for granted — and who are now building what comes next.
We foster a collaborative, inclusive environment where diverse perspectives drive better solutions — and where the best ideas win regardless of where they originate. We ship fast and iterate with purpose, and we believe work should be meaningful. You'll join a team that takes pride in building the platform the rest of the cloud stands on.
BASIC QUALIFICATIONS
10+ years of non-internship professional software development experience
Experience owning technical architecture, end-to-end delivery, and partnering with senior leadership
Track record of leading multiple concurrent technical initiatives and driving decisions that stuck
Experience influencing across organizational boundaries without positional authority
PREFERRED QUALIFICATIONS
15–25+ years of deep hands-on technical expertise in building complex distributed systems
Experience influencing VP/SVP-level technical and business strategy
Demonstrated ability to grow and develop Principal Engineers and senior technical talent
Experience with large-scale systems programming, virtualization, operating systems, or performance engineering
Familiarity with hardware-software co-design — you understand what happens below the abstraction, including firmware, PCI, kernels, and accelerator integration
History of shipping systems where latency, availability, and security are hard constraints, not goals
Experience solving customer and technical problems with durable, scalable solutions over multi-year, multi-billion dollar, and multi-organization impact
Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.
Los Angeles County applicants: Job duties for this position include: work safely and cooperatively with other employees, supervisors, and staff; adhere to standards of excellence despite stressful conditions; communicate effectively and respectfully with employees, supervisors, and staff to ensure exceptional customer service; and follow all federal, state, and local laws and Company policies. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness and professionalism, and safeguard business operations and the Company’s reputation. Pursuant to the Los Angeles County Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
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, CA, Cupertino - 230,100.00 - 311,200.00 USD annually
USA, CA, Santa Clara - 230,100.00 - 311,200.00 USD annually
USA, NY, New York - 220,100.00 - 297,700.00 USD annually
USA, VA, Arlington - 200,100.00 - 270,600.00 USD annually
USA, VA, Herndon - 200,100.00 - 270,600.00 USD annually
USA, WA, Seattle - 200,100.00 - 270,600.00 USD annually
Salary Context
This $200K-$297K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($248K) sits 17% above the category median. Disclosed range: $200K to $297K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; VP: $235,000.
Amazon.com AI Hiring
Amazon.com has 72 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, Data Scientist, AI Product Manager. Positions span Seattle, WA, US, New York, NY, US, Bellevue, WA, US. Compensation range: $129K - $327K.
Location Context
AI roles in New York pay a median of $221,000 across 2,878 tracked positions. That's 4% 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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