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About This Role
DESCRIPTION
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Do you think big about how Physical AI, robotics, and simulation technologies are converging with Generative AI to reshape industrial operations, autonomous systems, and the machines of tomorrow? Would you like a career that puts you at the frontier of AI innovation \- building prototypes that help customers harness digital twins, synthetic training data, and autonomous robots to solve real\-world problems at scale? Amazon Web Services (AWS) is looking for a Prototyping Architect who thrives at the intersection of cloud computing, Generative AI, and Physical AI \- someone who can turn a customer's vision of an intelligent physical system into a working prototype in weeks.
The AWS Prototyping and AI Customer Engineering (PACE) team transforms ambitious ideas into working prototypes \- faster than customers thought possible \- and we need world\-class builders who combine deep software craftsmanship with curiosity about physical systems. In this role, you'll work hands\-on with customers to architect and build production\-ready prototypes spanning the full spectrum of modern AI: from Generative AI and agentic workflows to simulation environments, synthetic data pipelines, and robotic model training. You'll leverage AWS AI services, NVIDIA's simulation and training stack, and cloud\-native architectures to rapidly deliver solutions that show what's possible when intelligence meets the physical world.
You'll be a full\-stack developer comfortable spanning multiple languages and frameworks, capable of making principled architectural decisions about agent behaviors, LLM integrations, AI\-driven simulation workflows, and digital twin architectures. You'll have foundational exposure to Physical AI domains \- digital twins, robotic or discrete event simulation, synthetic data generation with World Foundation Models, and robot policy training \- and bring the curiosity to go deeper. This isn't traditional consulting: you'll be writing code, designing simulation architectures, and building breakthrough experiences that shape how industrial and technology organizations globally approach Physical AI adoption.
Position requires travel \- generally 25%
Key job responsibilities
\- Architect and build working Generative AI, Agentic AI, and Physical AI prototypes directly with customers using AWS AI services (Bedrock, SageMaker, IoT TwinMaker, IoT SiteWise) and cloud\-native architectures \- including autonomous agents, RAG architectures, LLM\-powered applications, and simulation\-driven workflows that demonstrate production\-ready solutions
\- Design and build Physical AI prototypes spanning digital twin environments, discrete event simulation, robotic policy training pipelines, and synthetic data generation \- leveraging tools such as NVIDIA Omniverse, Isaac Sim, AWS VAMS, and AWS\-native compute and storage services
- Leverage AI\-driven development tools (Cursor, Kiro, Q Developer, Claude Code) to accelerate prototype development, implementing patterns like prompt engineering, function calling, agent orchestration, tool use, and simulation pipeline automation
\- Serve as a trusted technical advisor to customers on LLM selection, agent design, Physical AI architecture, and AI adoption strategy \- guiding them through complex technical decisions and trade\-offs across both software\-defined AI and physical systems
- Collaborate with Technical Program Managers, Design Technologists, and fellow Prototyping Architects to deliver customer engagements on time and with lasting impact, working across the full Physical AI flywheel from spatial data and simulation through to model training and deployment
- Create and share reusable patterns and thought leadership through simulation frameworks, code libraries, technical content, whitepapers, blogs, and conference presentations that accelerate both Generative AI and Physical AI adoption across the AWS customer base
- Work across the AWS ecosystem to distill customer needs and influence product features and roadmaps for Physical AI and simulation workloads, acting as a technical liaison between customers, service engineering teams, and AWS partners including NVIDIA
A day in the life
Your morning starts with team standup where you and fellow Prototyping Architects align with your Technical Program Manager on customer momentum, breakthrough patterns, and hard problems at Physical AI's edge. Then into deep coding work building transformative spatial solutions: constructing digital twins, integrating 3D perception with foundation models, and orchestrating real\-time spatial data pipelines on AWS. You make architectural decisions that drive measurable business outcomes: predictive maintenance through simulation, immersive training experiences, autonomous navigation in physical spaces.
Mid\-morning brings a strategic conversation with customer leadership on how Physical AI and spatial architectures power their business. Afternoon: architecting an immersive experience with specialized spatial agents, collaborating with Design Technologists on visualization. You document edge cases and patterns that become blueprints for future engagements. As day closes, you're context\-switching between production code, architectural decisions, and translating innovation into strategic language for leadership. You're building solutions that illuminate the path for how enterprises adopt Physical AI.
About the team
The PACE team accelerates innovation for AWS customers, setting the direction for enterprise adoption of transformative technologies. We're builders: Design Technologists, Technical Program Managers, and Prototyping Architects building solutions with world\-class engineers using emerging technologies. We operate at technology's bleeding edge \- embracing high\-judgment experimentation as a catalyst for breakthrough innovation across Generative AI, Agentic AI, and Physical AI.
You'll work alongside a team of Prototyping Architects who together cover the Physical AI innovation lifecycle \- from spatial data and 3D pipelines through simulation, synthetic data, model training, and edge deployment. We engage with customer executives, collaborate on novel problems, and drive impact globally across manufacturing, aerospace, retail, logistics, construction, and beyond.BASIC QUALIFICATIONS
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- 5\+ years of design, implementation, or consulting in applications and infrastructures experience
- Intellectual curiosity to keep technical skills current and stay abreast of industry trends and applications
- Demonstrated ability to adapt to new technologies and learn quickly
- BS level technical degree required; Computer Science or Mathematics background preferred.
- Excellent verbal skills and ability to explain technical concepts to a variety of audiences
- Strong written communication skills
PREFERRED QUALIFICATIONS
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- 3\-5 years of experience designing, implementing, or consulting with Augmented Reality (AR), Virtual Reality (VR), Mixed Reality, 3D, or immersive applications
- Intellectual curiosity to keep technical skills current and stay abreast of industry trends and applications. Demonstrated ability to adapt to new technologies and learn quickly.
- Comfort speaking with and presenting to executives, IT managers, and developers (internal and external)
- Experience architecting or operating solutions built on AWS
- Excellent verbal and written communication skills
- Experience with 3D geometry workflows, such as, 3D Scanning, Material and Texture Development, and Real Time Rendering.
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.
Pursuant to the San Francisco 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.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Amazon Web Services AI Hiring
Amazon Web Services has 73 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Scientist, Data Scientist. Positions span New York, NY, US, Austin, TX, US, Jersey City, NJ, US. Compensation range: $129K - $342K.
Location Context
AI roles in Seattle pay a median of $236,900 across 267 tracked positions. That's 9% 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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