Principal, Applied Scientist, AWS Applied AI Solutions

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• 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)
• 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

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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

Key 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

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

• 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