Prototyping Architect (Physical AI), AWS Prototyping and AI Customer Engineering (PACE)

📍 Job Overview

Job Title: Prototyping Architect (Physical AI), AWS Prototyping and AI Customer Engineering (PACE)

Company: Amazon

Location: Seattle, Washington; Arlington, Virginia; New York, New York; Mountain View, California; San Francisco, California

Job Type: Full-Time

Category: Cloud Computing & AI Engineering

Date Posted: August 10, 2026

Experience Level: 5+ Years

Remote Status: On-site

🚀 Role Summary

• Architect and build cutting-edge prototypes at the intersection of Physical AI, Generative AI, and cloud computing, leveraging AWS services to solve complex customer challenges.

• Drive customer adoption of advanced AI technologies by transforming ambitious ideas into tangible, production-ready prototypes within accelerated timelines.

• Serve as a technical leader and trusted advisor, guiding customers through architectural decisions for intelligent physical systems, including digital twins, simulation environments, and robotic training pipelines.

• Contribute to the development of reusable patterns, thought leadership, and technical content that advances the broader adoption of Generative AI and Physical AI across the AWS customer base.

📝 Enhancement Note: This role is positioned within the AWS Prototyping and AI Customer Engineering (PACE) team, indicating a strong focus on hands-on customer engagement and rapid solution development. The emphasis on "Physical AI" suggests a need for expertise bridging software-defined intelligence with real-world physical systems, robotics, and simulation. This is not a typical software development role; it requires a blend of deep technical skill, customer-facing advisory, and a passion for innovation at the technology frontier.

📈 Primary Responsibilities

• Architect and develop working prototypes for customers utilizing Generative AI, Agentic AI, and Physical AI technologies, including autonomous agents, RAG architectures, LLM-powered applications, and simulation-driven workflows.

• Design and implement Physical AI prototypes encompassing digital twin environments, discrete event simulation, robotic policy training pipelines, and synthetic data generation, leveraging tools like NVIDIA Omniverse, Isaac Sim, AWS VAMS, and AWS-native compute/storage services.

• Utilize AI-driven development tools (e.g., Cursor, Kiro, Q Developer, Claude Code) to accelerate prototype development, employing techniques such as prompt engineering, function calling, agent orchestration, tool use, and simulation pipeline automation.

• Act as a trusted technical advisor to customers, providing guidance on LLM selection, agent design, Physical AI architecture, and AI adoption strategies, navigating complex technical trade-offs between software and physical systems.

• Collaborate effectively with Technical Program Managers, Design Technologists, and fellow Prototyping Architects to ensure timely and impactful customer engagements across the Physical AI flywheel, from spatial data and simulation to model training and deployment.

• Create and disseminate reusable patterns, code libraries, technical content, whitepapers, blogs, and conference presentations to foster the adoption of Generative AI and Physical AI across the AWS customer base.

• Engage with the AWS ecosystem to gather customer needs, influence product features and roadmaps for Physical AI and simulation workloads, and serve as a technical liaison between customers, service engineering teams, and AWS partners like NVIDIA.

📝 Enhancement Note: The responsibilities highlight a hands-on, full-stack development approach with a specific focus on AI and simulation technologies. The role requires not only building prototypes but also acting as a technical consultant and influencing product development. The mention of the "Physical AI flywheel" suggests a need to understand the end-to-end lifecycle of AI in physical systems.

🎓 Skills & Qualifications

Education:

• Bachelor's degree required; a background in Computer Science or Mathematics is preferred. Experience:

• Minimum of 5+ years in application and infrastructure design, implementation, or consulting.

• Demonstrated ability to adapt to new technologies and learn quickly.

• Experience architecting or operating solutions built on AWS.

• 3-5 years of experience designing, implementing, or consulting with Augmented Reality (AR), Virtual Reality (VR), Mixed Reality, 3D, or immersive applications is preferred.

• Experience with 3D geometry workflows (e.g., 3D Scanning, Material and Texture Development, Real-Time Rendering) is preferred. Required Skills:

• Deep understanding and practical experience with Generative AI, including LLMs, RAG architectures, and agentic workflows.

• Proficiency in full-stack development across multiple languages and frameworks, with the ability to make principled architectural decisions.

• Strong technical advisory skills, capable of guiding customers through complex AI and cloud-native architectural decisions.

• Excellent verbal and written communication skills, with the ability to articulate technical concepts to diverse audiences, including executives.

• Intellectual curiosity to stay current with industry trends and continuously learn new technologies. Preferred Skills:

• Hands-on experience with Physical AI domains such as digital twins, robotic simulation, synthetic data generation (using World Foundation Models), and robot policy training.

• Familiarity with simulation and training stacks from NVIDIA, including Omniverse and Isaac Sim.

• Experience with AWS AI services such as Amazon Bedrock, Amazon SageMaker, AWS IoT TwinMaker, and AWS IoT SiteWise.

• Comfort in speaking with and presenting to executives, IT managers, and developers.

• Proficiency in AI-driven development tools like Cursor, Kiro, Q Developer, or Claude Code.

📝 Enhancement Note: The qualifications emphasize a blend of strong software engineering fundamentals, specific expertise in modern AI (Generative and Physical), and customer-facing consulting skills. The preference for experience in AR/VR/3D and specific AWS/NVIDIA tools indicates a highly specialized role within the emerging field of Physical AI.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate experience architecting and building complex prototypes, ideally showcasing work with AI, simulation, or cloud-native architectures.

• Provide examples of solutions that solve real-world customer problems, highlighting the impact and measurable outcomes achieved.

• Showcase projects involving the integration of multiple technologies and services, illustrating your ability to design comprehensive systems.

• Include case studies or project descriptions that detail the technical challenges faced, the architectural decisions made, and the rationale behind them. Process Documentation:

• Be prepared to discuss your approach to rapid prototyping, including methodologies for accelerating development cycles and delivering working solutions quickly.

• Document your understanding of the Physical AI flywheel, from spatial data capture and simulation to model training and deployment, and how you would architect solutions within this framework.

• Articulate your process for evaluating and selecting appropriate AI models (e.g., LLMs) and simulation tools based on customer requirements and technical constraints.

• Demonstrate a clear process for documenting technical decisions, reusable patterns, and best practices derived from customer engagements.

📝 Enhancement Note: While not explicitly stated as a formal portfolio requirement, the nature of this role—building prototypes and demonstrating technical leadership—necessitates a strong portfolio. Candidates should be prepared to showcase their ability to translate complex technical concepts into tangible prototypes and articulate their development processes.

💵 Compensation & Benefits

Salary Range:

• Seattle, Washington: $153,600 - $207,800 USD annually

• Arlington, Virginia: $153,600 - $207,800 USD annually

• New York, New York: $169,000 - $228,600 USD annually

• Mountain View, California: $176,600 - $239,000 USD annually

• San Francisco, California: $176,600 - $239,000 USD annually

Benefits:

• Comprehensive health insurance (medical, dental, vision, prescription coverage).

• Basic Life & AD&D insurance with options for supplemental life plans.

• Employee Assistance Program (EAP) and Mental Health Support.

• Medical Advice Line.

• Flexible Spending Accounts (FSAs).

• Adoption and Surrogacy Reimbursement coverage.

• 401(k) matching program.

• Paid Time Off (PTO).

• Parental Leave.

• Sign-on payments.

• Restricted Stock Units (RSUs). Working Hours:

• Standard 40 hours per week, with potential for flexibility depending on project needs and customer engagement schedules. Travel, estimated at 25%, will be a component of the role.

📝 Enhancement Note: The salary ranges provided are specific to location, reflecting the varying cost of living and market rates across the listed U.S. cities. The inclusion of sign-on payments and RSUs indicates a comprehensive compensation package typical for senior technical roles at Amazon.

🎯 Team & Company Context

🏢 Company Culture

Industry: Cloud Computing, Artificial Intelligence, E-commerce, Robotics, and Physical Systems.

Company Size: Amazon is a global technology giant with hundreds of thousands of employees worldwide, operating across diverse sectors.

Founded: 1994. Amazon has a long history of innovation, evolving from an online bookstore to a leader in cloud computing, AI, and various other technology domains.

Team Structure:

• The PACE (Prototyping and AI Customer Engineering) team is composed of specialized roles including Prototyping Architects, Technical Program Managers, and Design Technologists, all focused on accelerating customer innovation.

• This role likely reports into a management structure within AWS Global Sales or a dedicated AI/Prototyping division, with close collaboration across engineering, product, and customer-facing teams.

• Cross-functional collaboration is core, involving direct engagement with customers, AWS service teams, and key partners like NVIDIA to drive solution development and product influence. Methodology:

• Data-driven decision-making is paramount, utilizing customer insights and performance metrics to refine prototypes and strategies.

• Agile and iterative development methodologies are employed to rapidly build and deploy working prototypes, emphasizing speed and customer feedback.

• A culture of high-judgment experimentation is encouraged, particularly at the "bleeding edge" of technologies like Generative AI and Physical AI, to foster breakthrough innovation.

Company Website: https://www.amazon.com

📝 Enhancement Note: Amazon's culture is known for its customer obsession, bias for action, and a strong emphasis on long-term thinking and innovation. The PACE team specifically embodies these principles by focusing on rapid prototyping and pushing the boundaries of emerging technologies to solve customer problems.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a senior technical specialist, a "Prototyping Architect," indicating a high level of expertise and responsibility. It sits beyond entry-level or mid-level engineering roles, requiring significant experience in design, implementation, and advisory capacities.

Reporting Structure: The role involves close collaboration with Technical Program Managers and likely reports to a manager or director within the AWS Prototyping and AI Customer Engineering (PACE) organization. Direct interaction with customer executives and AWS service teams is also a key aspect.

Operations Impact: The primary impact is on accelerating customer adoption of AWS AI services, particularly in the nascent fields of Physical AI and Generative AI. By building tangible prototypes, this role directly influences customer strategies, drives innovation, and helps shape the future of industrial operations, autonomous systems, and intelligent physical machines. The role also influences AWS product roadmaps through customer feedback.

Growth Opportunities:

• Specialization: Deepen expertise in Physical AI, Generative AI, robotics, simulation, and specific AWS services, becoming a recognized authority in these emerging fields.

• Leadership: Transition into technical leadership roles, managing teams of architects or program managers, or taking on broader strategic responsibilities within AWS AI customer engineering.

• Product Influence: Further leverage customer insights to directly influence the development and roadmap of AWS AI services and related partner technologies.

• Thought Leadership: Grow as a public speaker and author, presenting at conferences and publishing content that establishes thought leadership in Physical AI and advanced prototyping.

• Cross-functional Mobility: Opportunities to move into related roles within AWS, such as Solutions Architecture, Product Management, or specialized AI/ML engineering teams.

📝 Enhancement Note: This role offers significant growth potential for individuals passionate about cutting-edge AI and physical systems. The emphasis on prototyping and customer engagement provides a direct path to influencing both customer success and AWS product strategy, leading to specialized leadership or broader technical career advancement.

🌐 Work Environment

Office Type: This is an on-site role, indicating a requirement to work from one of Amazon's designated office locations in Seattle, Arlington, New York, Mountain View, or San Francisco. The environment is expected to be collaborative and conducive to innovation.

Office Location(s):

• Seattle, Washington

• Arlington, Virginia

• New York, New York

• Mountain View, California

• San Francisco, California Workspace Context:

• The workspace will likely foster a collaborative environment, enabling close interaction with fellow Prototyping Architects, Technical Program Managers, and Design Technologists.

• Access to state-of-the-art tools and technologies, including AWS services, NVIDIA simulation platforms, and advanced development environments, will be provided.

• Opportunities for frequent interaction with customers, both on-site and potentially at customer locations (up to 25% travel), will be integral to the role.

Work Schedule: The role is full-time, with a standard expectation of 40 hours per week. However, the dynamic nature of customer projects and prototype development may require flexibility and dedication to meet project deadlines and customer needs. Travel is expected to be around 25%.

📝 Enhancement Note: The on-site requirement suggests a preference for in-person collaboration, which is often crucial for rapid prototyping and complex problem-solving. The travel component indicates a significant customer-facing aspect to the role, requiring adaptability and strong communication skills in various settings.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will likely review your application and conduct an initial phone screen to assess basic qualifications and interest.

• Technical Phone/Video Interviews: Expect multiple rounds of interviews focusing on your technical skills, including coding challenges, system design questions, and scenario-based problem-solving related to AI, cloud architecture, and potentially simulation/robotics.

• Prototyping/Architecture Deep Dive: A significant portion of the interview process will likely involve discussing past projects, architectural decisions, and potentially presenting a portfolio of your work. This may include a live coding exercise or a detailed explanation of a complex prototype you've built.

• Behavioral Interviews: Questions will assess your ability to collaborate, communicate effectively, handle ambiguity, adapt to new technologies, and demonstrate intellectual curiosity, aligning with Amazon's Leadership Principles.

• Bar Raiser Interview: A final interview conducted by a trained "Bar Raiser" whose role is to ensure the candidate meets Amazon's high hiring bar. This interview often focuses on behavioral aspects and long-term potential.

Portfolio Review Tips:

• Showcase Impact: Focus on projects where you architected and built solutions that delivered tangible customer value or business impact. Quantify results whenever possible (e.g., performance improvements, cost savings, accelerated timelines).

• Highlight AI & Cloud Expertise: Prioritize projects demonstrating your skills in Generative AI, Physical AI, cloud-native architectures, and relevant AWS services. If you have experience with simulation, digital twins, or robotics, ensure these are prominently featured.

• Detail Your Process: Be prepared to walk through your design process, architectural decisions, the tools and technologies you used, and the trade-offs you made. Explain why you made certain choices.

• Demonstrate Prototyping Skills: If possible, include examples of rapid prototyping, proof-of-concepts, or MVPs you've developed. Showcase your ability to translate ideas into working solutions quickly.

• Technical Depth & Breadth: Ensure your portfolio demonstrates both deep technical expertise in key areas and a broad understanding of related technologies and systems.

Challenge Preparation:

• Coding: Practice coding challenges focusing on algorithms, data structures, and potentially AI-specific implementations (e.g., basic LLM integration, data processing for simulation). LeetCode (medium/hard) and HackerRank are good resources.

• System Design: Prepare for system design questions, focusing on how to architect scalable, resilient, and performant systems, particularly those involving AI components, data pipelines, and potentially distributed systems.

• AI/ML Concepts: Review core concepts of Generative AI, LLMs, RAG, agentic workflows, and foundational principles of Physical AI, simulation, and digital twins.

• AWS Knowledge: Refresh your understanding of key AWS services, especially those mentioned in the job description (Bedrock, SageMaker, IoT TwinMaker, IoT SiteWise).

• Behavioral Questions: Prepare STAR method (Situation, Task, Action, Result) responses for common behavioral questions, drawing examples from your experience that align with Amazon's Leadership Principles.

📝 Enhancement Note: The interview process at Amazon is rigorous and multi-faceted. Candidates should prepare thoroughly across technical, behavioral, and architectural domains, with a strong emphasis on demonstrating their ability to build and innovate with AI and cloud technologies, as evidenced by their past work.

🛠 Tools & Technology Stack

Primary Tools:

• AWS AI Services: Amazon Bedrock, Amazon SageMaker, AWS IoT TwinMaker, AWS IoT SiteWise. Proficiency in these is crucial for building customer solutions.

• NVIDIA Stack: NVIDIA Omniverse, NVIDIA Isaac Sim. Essential for developing Physical AI prototypes involving simulation, digital twins, and robotics.

• Development Tools: Cursor, Kiro, Q Developer, Claude Code (or similar AI-assisted coding tools). Expected for accelerating prototype development.

• Cloud-Native Architectures: Understanding and experience with building applications on cloud infrastructure, including compute (EC2, Lambda), storage (S3, EBS), and networking services.

Analytics & Reporting:

• Tools for analyzing prototype performance, customer usage data, and simulation outcomes. This may include AWS analytics services (e.g., CloudWatch, QuickSight) or custom solutions.

• Data visualization tools to present findings and prototype capabilities to customers and internal stakeholders. CRM & Automation:

• While not explicitly mentioned for the architect role, familiarity with CRM systems (like Salesforce) could be beneficial for understanding customer engagement context.

• Experience with workflow automation tools and CI/CD pipelines for streamlining prototype development and deployment processes.

• Integration tools and APIs for connecting various services and data sources within complex prototypes.

📝 Enhancement Note: The technology stack is heavily focused on AWS's AI and IoT offerings, combined with NVIDIA's powerful simulation and AI development platforms. This indicates a need for deep expertise in these specific areas, along with strong general cloud-native development skills.

👥 Team Culture & Values

Operations Values:

• Customer Obsession: A relentless focus on understanding and meeting customer needs through innovative prototypes and solutions.

• Bias for Action: A drive to build, experiment, and deliver results quickly, even with incomplete information, to accelerate learning and customer impact.

• Invent and Simplify: Continuously seeking new ways to solve problems and simplifying complex technical challenges into elegant, effective solutions.

• Dive Deep: A commitment to understanding the intricacies of customer problems, technical challenges, and the underlying technologies.

• Intellectual Curiosity: A passion for learning and staying at the forefront of rapidly evolving fields like Generative AI and Physical AI.

Collaboration Style:

• Cross-functional Integration: Working closely with Technical Program Managers, Design Technologists, and customer teams to ensure seamless prototype development and delivery.

• Open Communication: Encouraging candid feedback and transparent communication, especially when facing complex technical challenges or making difficult architectural decisions.

• Knowledge Sharing: Actively contributing to a culture of learning by sharing insights, patterns, and best practices through documentation, presentations, and internal discussions.

📝 Enhancement Note: The team culture is heavily influenced by Amazon's core Leadership Principles, emphasizing a proactive, customer-centric, and innovative approach to problem-solving. Collaboration is key, driven by a shared mission to accelerate customer adoption of advanced AI technologies.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving Landscape: Staying current with the fast-paced advancements in Generative AI, Physical AI, robotics, and simulation technologies requires continuous learning and adaptation.

• Bridging Physical and Digital: Effectively integrating complex software-defined AI with real-world physical systems and simulation environments presents unique technical hurdles.

• Customer Complexity: Each customer will have unique challenges and varying levels of technical maturity, requiring tailored solutions and effective communication.

• Proof of Concept to Production: Translating experimental prototypes into solutions that demonstrate production readiness and scalability requires careful architectural planning.

Learning & Development Opportunities:

• Deep Specialization: Gain unparalleled expertise in the cutting edge of Physical AI, Generative AI, and cloud-native development for intelligent physical systems.

• Industry Exposure: Work with a diverse range of global customers across various industries (manufacturing, aerospace, retail, logistics, construction) to understand their unique operational challenges.

• NVIDIA & AWS Ecosystem: Develop deep proficiency with leading-edge tools and platforms from AWS and NVIDIA, becoming a go-to expert in their application.

• Thought Leadership Development: Opportunities to present at industry events, publish technical content, and influence the direction of AI adoption in physical domains.

• Mentorship: Learn from and collaborate with experienced architects and program managers within a high-performing team focused on innovation.

📝 Enhancement Note: This role presents significant challenges due to the frontier nature of the technologies involved. However, these challenges are directly tied to substantial growth opportunities for individuals looking to become experts in the emerging field of Physical AI and its integration with cloud computing.

💡 Interview Preparation

Strategy Questions:

• Scenario-Based AI Architecture: "Describe how you would architect a system for an autonomous warehouse using Generative AI for pathfinding and physical robots for material handling. What AWS services would you use, and what are the key challenges?" (Focus on agent orchestration, LLM integration, simulation, and AWS IoT services).

• Prototyping Methodology: "Walk me through your process for taking a customer's abstract idea for a 'smart factory' and turning it into a working prototype within 4-6 weeks. What are the critical steps and decision points?" (Highlight rapid development, iteration, and customer feedback loops).

• Physical AI Integration: "How would you use digital twins and synthetic data to train a robotic arm for a complex assembly task? Discuss the role of simulation and potential AWS services." (Focus on NVIDIA Omniverse/Isaac Sim, AWS IoT TwinMaker, and synthetic data generation).

Company & Culture Questions:

• Amazon Leadership Principles: Prepare specific examples for each principle (Customer Obsession, Ownership, Invent and Simplify, etc.) that demonstrate your alignment with Amazon's culture.

• Collaboration: "Describe a time you had to collaborate with engineers from different disciplines (e.g., software, hardware, simulation) to achieve a common goal. What were the challenges, and how did you overcome them?"

• Learning & Adaptation: "Tell me about a time you had to quickly learn a new, complex technology to complete a project. How did you approach it, and what was the outcome?"

Portfolio Presentation Strategy:

• Storytelling: Structure your portfolio presentations around compelling narratives that highlight the customer problem, your innovative solution, the technical execution, and the resulting impact.

• Visuals: Use diagrams, architecture schematics, and demo videos where possible to illustrate complex systems and prototype functionality.

• Technical Depth: Be ready to dive deep into the technical details of your projects, explaining architectural choices, algorithms, and specific technologies used.

• Quantify Impact: Whenever possible, use metrics to demonstrate the value and success of your projects (e.g., performance gains, efficiency improvements, cost reductions).

• Focus on AI & Cloud: Ensure your presented projects clearly showcase your expertise in AI (especially Generative and Physical AI) and cloud platforms like AWS.

📝 Enhancement Note: Preparation should focus on demonstrating a blend of deep technical expertise in AI and cloud computing, a strong customer-centric approach, and the ability to innovate rapidly. Understanding Amazon's Leadership Principles and being able to articulate your experiences through the STAR method is critical.

📌 Application Steps

To apply for this Prototyping Architect position:

• Submit your application through the Amazon Jobs portal via the provided URL.

• Portfolio Customization: Tailor your resume and any supplementary materials to highlight your experience with Generative AI, Physical AI, AWS services, simulation technologies, and full-stack development. Emphasize projects involving prototyping and customer engagement.

• Resume Optimization: Ensure your resume clearly articulates your 5+ years of experience in design, implementation, or consulting, with specific achievements and quantifiable results. Integrate keywords from the job description naturally.

• Interview Preparation: Thoroughly review Amazon's Leadership Principles and prepare STAR method examples. Practice coding and system design questions, with a particular focus on AI/ML and cloud architecture. Be ready to discuss your portfolio in detail.

• Company Research: Familiarize yourself with Amazon Web Services (AWS), its AI offerings, and the company's overall mission and values. Understand the role of the PACE team and the significance of Physical AI.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.