AmazonPosted 7mo ago

Principal, Applied Scientist, AWS Applied AI Solutions

Principal, Applied Scientist, AWS Applied AI Solutions at Amazon scores 99 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

US, WA, Seattleleadfull-time$199k-$269k

AI in this role

pytorchtensorflow

computer-vision

Join us if you're excited about pushing the boundaries of what's possible in physical AI, working with world-class scientists and engineers, and seeing your innovations deployed at unprecedented scale.

We are seeking an exceptional Principal Applied Scientist to drive technical innovation in visual reasoning foundation models. You will be a technical leader who sets the research direction, architects novel solutions, and delivers breakthrough results that advance the state of the art while solving real-world business problems.

You will lead the efforts of building a next-generation visual reasoning engine powered by frontier Large Video Models (LVMs). Your mission is to build a system that rivals human understanding of the physical world—moving far beyond the static perception of detection and tracking into the realm of deep spatial-temporal reasoning. This is not a passive computer vision tool; it is an agentic collaborator capable of interpreting natural language instructions, navigating unstructured environments, and executing complex tasks.

You will sit at the high-stakes intersection of LVMs, LLMs, and Agentic AI, engineering systems that don't just 'see' but reason and act within the physical world. You will own end-to-end technical solutions from research to production deployment, driving innovation through hands-on research, prototyping, and deployment while delivering production impact.

Key job responsibilities
- Direct the technical vision for next-gen visual reasoning, pioneering the use of LVMs to solve high-dimensional spatial-temporal problems
- Design and implement novel deep learning architectures combining a multitude of modalities, including image, video, and geospatial data
- Solve computational challenges to train foundation models at scale, taking advantage of latest developments in hardware and deep learning libraries
- Architect scalable solutions that deliver real-time insights across diverse physical environments
- Build agentic AI systems that autonomously execute end-to-end workflows, transforming visual data into actionable business intelligence
- Collaborate with multiple science and engineering teams to build adaptations that power use cases across diverse domains
- Publish research at top-tier conferences (CVPR, NeurIPS, ICML) and establish technical thought leadership in visual reasoning and multi-modal AI
- Mentor scientists and engineers while maintaining significant hands-on contribution to technical solutions
- Influence product roadmaps through deep technical expertise and business acumen

A day in the life
- Develop and implement novel foundation model architectures, working hands-on with data and our extensive training and evaluation infrastructure
- Guide and support fellow scientists in solving complex technical challenges, from spatiotemporal reasoning to efficient multi-task learning
- Guide and support fellow engineers in building scalable and reusable infrastructure to support model training, evaluation, and inference
- Lead focused technical initiatives from conception through deployment, ensuring successful integration with production systems
- Drive technical discussions with the team and key stakeholders
- Conduct experiments and prototype new ideas
- Mentor team members while maintaining significant hands-on contribution to technical solutions

About the team
As part of the AWS Applied AI Solutions organization, we have a vision to provide business applications, leveraging Amazon's unique experience and expertise, that are used by millions of companies worldwide to manage day-to-day operations. We will accomplish this by accelerating our customers' businesses through delivery of intuitive and differentiated technology solutions that solve enduring business challenges. 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 buy and easy to use.

Join the next science and engineering revolution at AWS Applied AI Solutions, where you'll work alongside world-class scientists and engineers to pioneer the next frontier of visual reasoning through advanced AI and foundation models.

Our team builds cutting-edge visual reasoning systems powered by vision-language-reasoning models to understand complex behaviors in the physical world. Our algorithms process video streams to reason about people, objects, and activities in real-time—enabling automated understanding of physical environments at scale. We develop state-of-the-art foundation models that push the boundaries of spatiotemporal reasoning, moving far beyond static perception.

We are building a visual reasoning foundation model that generalizes across diverse physical environments and domains. This represents a significant opportunity to develop frontier vision-language models, multi-modal AI, and agentic AI technologies that enable automated decision-making capabilities with massive business impact. We build everything end to end, from data curation to model training, evaluation, and inference, along with all the tooling needed to understand and analyze model performance.

Basic qualifications

- PhD in computer science, machine learning, engineering, or related fields
- 8+ years of applied research experience
- Have publications on top-tier conferences, such as CVPR, ICCV, ECCV or NeurIPS
- Deep expertise in architecting and training frontier Vision-Language Models (VLMs) or Large Video Models (LVMs), with proven ability to design novel algorithms that advance the state of the art in spatiotemporal understanding
- Experience translating research into production systems at scale
- Excellent programming skills in Python and deep learning frameworks (PyTorch, TensorFlow)

Preferred qualifications

- Deep expertise in World Models, Neural Radiance Fields (NeRFs/Gaussian Splatting), and long-horizon spatiotemporal reasoning
- Demonstrated leadership in building and scaling agentic AI applications in production
- A history of building and scaling world-class teams and mentoring senior ICs to achieve career breakthroughs

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, Seattle - 198,900.00 - 269,000.00 USD annually

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A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Computer VisionPyTorchTensorFlow

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
Walk me through how you've used PyTorch in your day-to-day work.
What are the limits of TensorFlow that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

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List these exact terms on your resume: Computer Vision, PyTorch, and TensorFlow. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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