Description
- Graduate degree in Computer science/Math or related field.
- Experience in building complex, real-time systems involving Agentic AI, Personalization and Reinforced Learning with successful delivery to customers. Demonstrated track record of project delivery for large, cross-functional projects with evolving requirements.
- Computer Science fundamentals in data structures, algorithm design and complexity analysis.
- Ability to develop machine learning platform strategy in the domain of recommender systems.
- Demonstrated track record of peer-reviewed scientific publications that advance state-of-the art for applied science.
- 15+ years of relevant, broad research experience after PhD degree or equivalent.
- Ability to take a project from requirements gathering and design to actual product launch
- Exceptional customer understanding skills including the ability to discover the true challenges to efficient product discovery, and experience in leading science efforts to meet timelines with optimal solutions. Deep expertise in Machine Learning as applied to large-scale generative models Proficiency in programming for algorithm and code reviews.
- Strong core competency in mathematics and statistics.
- Track record of successful projects in algorithm design and product development.
- Publications at top-tier peer-reviewed conferences or journals.
- Strong prior experience with mentorship and/or management of senior scientists and engineers.
- Thinks strategically, but stays on top of tactical execution.
- Exhibits excellent business judgment; balances business, product, and technology very well.
- Effective verbal and written communication skills with non-technical and technical audiences. Experience working with real-world data sets and building scalable models from big data.
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.
USA, NY, New York - 264,100.00 - 350,000.00 USD annually
USA, WA, Seattle - 240,100.00 - 324,800.00 USD annually
At Secure Work Enablement (SWE), we're pioneering breakthrough AI technologies that are fundamentally transforming how millions of teams and businesses work. Our mission combines leading machine learning research with Amazon's unparalleled expertise in enterprise computing and security to create the next generation of intelligent workplace solutions.
Our portfolio encompasses four innovative domains where applied AI research is critical: Next-Generation End User Computing, where we're developing novel machine learning models for human-AI collaboration; Amazon One's advanced biometric systems; Secure Collaboration platforms Wickr and Chime; and Gaia – our revolutionary AI-native workspace that's redefining human-AI agent interactions. Each area presents unique opportunities for advancing the state-of-the-art in machine learning, natural language processing, and AI systems.
We're at an inflection point where traditional workplace computing is being revolutionized by AI technologies. Our distinctive challenge lies in solving complex machine learning problems at scale while maintaining strict security requirements – from developing sophisticated ML models for secure information access to creating intelligent systems that can understand and enhance human productivity in real-time.
With an accomplished team of 650+ technologists and multiple tier-1 services, we're seeking a Senior Principal Scientist to spearhead our AI research and development initiatives. This role offers the opportunity to tackle unprecedented challenges in applied machine learning, including:
- Developing novel AI architectures for secure, enterprise-grade collaborative systems
- Advancing the science of human-AI interaction in workplace environments
- Creating new frameworks for AI agent orchestration and optimization
- Pioneering new approaches to privacy-preserving machine learning
Your scientific leadership will be crucial in defining and solving complex AI problems that will shape the future of work for years to come, working across multiple research teams and beyond SWE's boundaries.
Key job responsibilities
As a Senior Principal Scientist in Secure Work Enablement (SWE), you will have deep subject matter expertise in the area of large language models and generative AI across various modalities. You will work with multiple teams of scientists and engineers to translate business and functional requirements into concrete deliverables. You will invent new product experiences that enable teams and agents to collaborate effectively. You will have the opportunity to invent new approaches that help our customers achieve better results using natural language as the interface. Your inputs will shape the future of work.
You will liaise with internal Amazon partners and work on bringing state-of-the-art LLM/GenAI models to production. You will stay abreast of the latest developments in the field of GenAI and identify opportunities to improve the efficiency and productivity of the team. You will define a long-term science vision for our business, driven by our customer’s needs, and translate it into actionable plans for our team of applied scientists, and engineers. Finally, you will work with academic partners to support our in-house talent with direct access to leading research and mentoring.
- Experience in building complex, real-time systems involving Agentic AI, Personalization and Reinforced Learning with successful delivery to customers. Demonstrated track record of project delivery for large, cross-functional projects with evolving requirements.
- Computer Science fundamentals in data structures, algorithm design and complexity analysis.
- Ability to develop machine learning platform strategy in the domain of recommender systems.
- Demonstrated track record of peer-reviewed scientific publications that advance state-of-the art for applied science.
- 15+ years of relevant, broad research experience after PhD degree or equivalent.
- Ability to take a project from requirements gathering and design to actual product launch
- Exceptional customer understanding skills including the ability to discover the true challenges to efficient product discovery, and experience in leading science efforts to meet timelines with optimal solutions. Deep expertise in Machine Learning as applied to large-scale generative models Proficiency in programming for algorithm and code reviews.
- Strong core competency in mathematics and statistics.
- Track record of successful projects in algorithm design and product development.
- Publications at top-tier peer-reviewed conferences or journals.
- Strong prior experience with mentorship and/or management of senior scientists and engineers.
- Thinks strategically, but stays on top of tactical execution.
- Exhibits excellent business judgment; balances business, product, and technology very well.
- Effective verbal and written communication skills with non-technical and technical audiences. Experience working with real-world data sets and building scalable models from big data.
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, NY, New York - 264,100.00 - 350,000.00 USD annually
USA, WA, Seattle - 240,100.00 - 324,800.00 USD annually
At Secure Work Enablement (SWE), we're pioneering breakthrough AI technologies that are fundamentally transforming how millions of teams and businesses work. Our mission combines leading machine learning research with Amazon's unparalleled expertise in enterprise computing and security to create the next generation of intelligent workplace solutions.
Our portfolio encompasses four innovative domains where applied AI research is critical: Next-Generation End User Computing, where we're developing novel machine learning models for human-AI collaboration; Amazon One's advanced biometric systems; Secure Collaboration platforms Wickr and Chime; and Gaia – our revolutionary AI-native workspace that's redefining human-AI agent interactions. Each area presents unique opportunities for advancing the state-of-the-art in machine learning, natural language processing, and AI systems.
We're at an inflection point where traditional workplace computing is being revolutionized by AI technologies. Our distinctive challenge lies in solving complex machine learning problems at scale while maintaining strict security requirements – from developing sophisticated ML models for secure information access to creating intelligent systems that can understand and enhance human productivity in real-time.
With an accomplished team of 650+ technologists and multiple tier-1 services, we're seeking a Senior Principal Scientist to spearhead our AI research and development initiatives. This role offers the opportunity to tackle unprecedented challenges in applied machine learning, including:
- Developing novel AI architectures for secure, enterprise-grade collaborative systems
- Advancing the science of human-AI interaction in workplace environments
- Creating new frameworks for AI agent orchestration and optimization
- Pioneering new approaches to privacy-preserving machine learning
Your scientific leadership will be crucial in defining and solving complex AI problems that will shape the future of work for years to come, working across multiple research teams and beyond SWE's boundaries.
Key job responsibilities
As a Senior Principal Scientist in Secure Work Enablement (SWE), you will have deep subject matter expertise in the area of large language models and generative AI across various modalities. You will work with multiple teams of scientists and engineers to translate business and functional requirements into concrete deliverables. You will invent new product experiences that enable teams and agents to collaborate effectively. You will have the opportunity to invent new approaches that help our customers achieve better results using natural language as the interface. Your inputs will shape the future of work.
You will liaise with internal Amazon partners and work on bringing state-of-the-art LLM/GenAI models to production. You will stay abreast of the latest developments in the field of GenAI and identify opportunities to improve the efficiency and productivity of the team. You will define a long-term science vision for our business, driven by our customer’s needs, and translate it into actionable plans for our team of applied scientists, and engineers. Finally, you will work with academic partners to support our in-house talent with direct access to leading research and mentoring.
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