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applied scientist, agi

applied scientist

Amazon.com

<div><h2 class="jobSectionHeader">DESCRIPTION</h2><div>
The Artificial General Intelligence (AGI) team is looking for a highly skilled and experienced Applied Scientist, to support the development and implementation of state-of-the-art algorithms and models for supervised fine-tuning and reinforcement learning through human feedback and and complex reasoning; with a focus across text, image, and video modalities. 
 
As an Applied Scientist, you will play a critical role in supporting the development of Generative AI (Gen AI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. 
 
Key job responsibilities 
<ul><li>Collaborate with cross-functional teams of engineers, product managers, and scientists to identify and solve complex problems in Gen AI</li><li>Design and execute experiments to evaluate the performance of different algorithms and models, and iterate quickly to improve results</li><li>Think big about the arc of development of Gen AI over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems</li><li>Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports</li></ul></div><h2 class="jobSectionHeader">
BASIC QUALIFICATIONS</h2><ul><li>3+ years of building models for business application experience</li><li>PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience</li><li>Experience programming in Java, C++, Python or related language</li><li>Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>PhD in computer science, machine learning, engineering, or related fields</li><li>Experience or publications related to Agentic AI applications</li><li>Programming skills in Python and experience with deep learning frameworks such as Tensor Flow or PyTorch</li><li>Excellent problem-solving skills, with the ability to think creatively and critically about complex problems</li><li>Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams</li><li>Experience with patents or publications at top-tier peer-reviewed conferences or journals</li></ul> 
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&rsquo;s reputation. Pursuant to the Los Angeles County 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&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner. 
 
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.</div>

Applied Scientist, AGI

Thomson Reuters

<div>New Position: This position is open due to an existing vacancy to support our evolving business needs. 
Document understanding is a foundational intelligence layer that powers every major capability across our legal AI platform&mdash;from search and information extraction to agentic reasoning in products like Westlaw, PracticalLaw, and CoCounsel.
 
 
You'll build state-of-the-art semantic chunking, document enrichment, and knowledge graph construction systems that serve as the cognitive foundation multiple product teams depend on, working across authoritative legal, tax and accounting content and extraordinarily diverse customer data.
 
 
This is a rare opportunity to solve publishing-quality research problems with immediate production impact&mdash;your innovations will directly shape how millions of legal professionals research, analyze, and reason over complex legal documents while advancing the capabilities that enable the next generation of intelligent legal AI agents.
About the role
As an Senior Applied Scientist you will: 
<ul><li>Innovate &amp; Deliver: Design, build, test, and deploy end-to-end AI solutions for complex document understanding tasks in the legal domain. Develop advanced models for semantic chunking of lengthy, non-uniformly structured legal documents with adjustable granularity levels for different use cases. Build document enrichment systems that classify documents according to legal and customer-defined taxonomies and extract rich metadata. Create LLM-based knowledge graph construction pipelines that extract and link heterogeneous legal knowledge including citations, entities, and legal concepts across diverse legal content. Develop scalable synthetic data generation systems to support model training, simulate complex legal research queries and generate hallucination-free answers. Work in collaboration with engineering to ensure well-managed software delivery and reliability at scale. 
 
</li><li>Evaluate &amp; Optimize: Develop comprehensive data and evaluation strategies for both component-level and end-to-end quality, leveraging expert human annotation and synthetic data generation. Apply robust training and evaluation methodologies that balance model performance with latency requirements, particularly for SLM-based solutions. Apply knowledge distillation techniques to compress large models into efficient SLMs suitable for production deployment. 
 
</li><li>Drive Technical Decisions: Independently determine appropriate architectures for challenging document understanding problems including: semantic chunking strategies that handle diverse document formats, preserve legal document structure, and adapt to different granularity needs; document classification approaches that work across varying legal taxonomies and generalize to customer-defined schemas; LLM-based knowledge extraction methods that handle challenges like citation recognition errors and contextual references; multi-document reasoning architectures for generating synthetic multi-hop queries that reflect complex legal research patterns. Balance accuracy, efficiency, and scalability while solving real-world challenges like handling diverse document formats and content types. 
 
</li><li>Align &amp; Communicate: Partner closely with Engineering and Product teams to translate complex legal document understanding challenges into scalable, production-ready solutions. Engage stakeholders across multiple product lines to deeply understand use case requirements, shaping objectives that align document understanding capabilities with diverse business needs including next-generation search and deep legal research. 
 
</li><li>Advance the Field : Maintain scientific and technical expertise in one or more relevant areas as demonstrated through product deliverables, published research at top venues (e.g., ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD) , and intellectual property. 
</li></ul>About You
<ul><li>PhD in Computer Science, AI, NLP, or a related field, or a Master's with equivalent research/industry experience 
 
</li><li>5+ years of hands-on experience building and deploying document understanding systems, information extraction pipelines, or knowledge graph construction using deep learning, LLMs and NLP methods 
 
</li><li>Proven ability to translate complex document understanding problems into innovative AI applications that balance accuracy and efficiency 
 
</li><li>Professional experience scaling yourself and leading through others, in an applied research setting 
 
</li><li>Strong programming skills (e.g., Python) and experience with modern deep learning frameworks (e.g., PyTorch, Hugging Face Transformers, DeepSpeed) 
 
</li><li>Publications at relevant venues such as ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD 
</li></ul>Technical Qualifications
<ul><li>Deep understanding of document understanding fundamentals: document layout analysis, semantic chunking approaches beyond fixed-size or paragraph-based methods, document classification handling hierarchical taxonomies, imbalanced multi-label classification, and adapting to domain-specific schemas 
 
</li><li>Expertise in knowledge extraction and knowledge graph construction: entity recognition and linking, relation extraction, citation parsing, and building graph representations from unstructured text 
 
</li><li>Expertise in LLM-based information extraction, few-shot and multi-task learning, post-training and knowledge distillation 
 
</li><li>Solid understanding of synthetic data generation techniques for NLP, including query - answer generation with verification and scalable data augmentation for training specialized models 
 
</li><li>Solid understanding of efficiency optimization including knowledge distillation, model compression, and designing SLM-based solutions that balance performance with computational constraints 
 
</li><li>Solid understanding of DL/ML approaches used for NLP tasks 
 
</li><li>Experience designing annotation workflows, creating high-quality labeled datasets with clear guidelines, and developing evaluation frameworks for document understanding tasks 
</li></ul>Preferred Qualifications
<ul><li>Prior work on legal document understanding, legal information extraction, knowledge representation including legal citations and legal domain concepts or legal AI applications 
 
</li><li>Prior work handling complex document structures common in legal documents: non-uniform formatting, nested hierarchies, cross-references, and embedded elements 
 
</li><li>Experience with building systems that perform analysis, question answering or retrieval across large document collections 
 
</li><li>Experience with knowledge graph frameworks and methodologies for legal or enterprise applications 
 
</li><li>Understanding of RAG and agentic workflows for enterprise knowledge 
 
</li><li>Publications at relevant venues such as ACL, EMNLP, ICLR, NeurIPS, SIGIR, KDD 
 
</li><li>Experience working with AzureML or AWS SageMaker
</li></ul>#LI-LP2
What&rsquo;s in it For You?
<ul><li>Hybrid Work Model: We&rsquo;ve adopted a flexible hybrid working environment (2-3 days a week in the office depending on the role) for our office-based roles while delivering a seamless experience that is digitally and physically connected.
</li><li>Flexibility &amp; Work-Life Balance: Flex My Way is a set of supportive workplace policies designed to help manage personal and professional responsibilities, whether caring for family, giving back to the community, or finding time to refresh and reset. This builds upon our flexible work arrangements, including work from anywhere for up to 8 weeks per year, empowering employees to achieve a better work-life balance.
</li><li>Career Development and Growth: By fostering a culture of continuous learning and skill development, we prepare our talent to tackle tomorrow&rsquo;s challenges and deliver real-world solutions. Our Grow My Way programming and skills-first approach ensures you have the tools and knowledge to grow, lead, and thrive in an AI-enabled future.
</li><li>Industry Competitive Benefits: We offer comprehensive benefit plans to include flexible vacation, two company-wide Mental Health Days off, access to the Headspace app, retirement savings, tuition reimbursement, employee incentive programs, and resources for mental, physical, and financial wellbeing.
</li><li>Culture: Globally recognized, award-winning reputation for inclusion and belonging, flexibility, work-life balance, and more. We live by our values: Obsess over our Customers, Compete to Win, Challenge (Y)our Thinking, Act Fast / Learn Fast, and Stronger Together.
</li><li>Social Impact: Make an impact in your community with our Social Impact Institute. We offer employees two paid volunteer days off annually and opportunities to get involved with pro-bono consulting projects and Environmental, Social, and Governance (ESG) initiatives.
</li><li>Making a Real-World Impact: We are one of the few companies globally that helps its customers pursue justice, truth, and transparency. Together, with the professionals and institutions we serve, we help uphold the rule of law, turn the wheels of commerce, catch bad actors, report the facts, and provide trusted, unbiased information to people all over the world.
</li></ul>Our use of AI within the recruitment process Thomson Reuters utilizes Artificial Intelligence (AI) to support parts of our global recruitment process. Unless you opt-out, our AI system will assess the information provided by you and compare it to the requirements listed for the role, and present the result to our recruitment personnel for further review. The AI system acts as a supporting tool, but there is always a human making the decision if you will be considered for the role. 
In the United States, Thomson Reuters offers a comprehensive benefits package to our employees. Our benefit package includes market competitive health, dental, vision, disability, and life insurance programs, as well as a competitive 401k plan with company match. In addition, Thomson Reuters offers market leading work life benefits with competitive vacation, sick and safe paid time off, paid holidays (including two company mental health days off), parental leave, sabbatical leave. These benefits meet or exceeds the requirements of paid time off in accordance with any applicable state or municipal laws. Finally, Thomson Reuters offers the following additional benefits: optional hospital, accident and sickness insurance paid 100% by the employee; optional life and AD&amp;D insurance paid 100% by the employee; Flexible Spending and Health Savings Accounts; fitness reimbursement; access to Employee Assistance Program; Group Legal Identity Theft Protection benefit paid 100% by employee; access to 529 Plan; commuter benefits; Adoption &amp; Surrogacy Assistance; Tuition Reimbursement; and access to Employee Stock Purchase Plan.
Thomson Reuters complies with local laws that require upfront disclosure of the expected pay range for a position. The base compensation range varies across locations.&amp;#xa;&amp;#xa;Eligible office location(s) for this role include one or more of the following: New York City, San Francisco, Los Angeles, and/or Irvine, CA; McLean, VA; Washington, DC. The base compensation range for the role in any of those locations is $145,200 USD - $269,600 USD.&amp;#xa;For any eligible US locations, unless otherwise noted, the base compensation range for this role is $126,000 USD - $234,000 USD.&amp;#xa;For Ontario, Canada, the base compensation range for this role is $100,000 CAD - $145,000 CAD.&amp;#xa;&amp;#xa;Base pay is positioned within the range based on several factors including an individual&rsquo;s knowledge, skills and experience with consideration given to internal equity. Base pay is one part of a comprehensive Total Reward program which also includes flexible and supportive benefits and other wellbeing programs.&amp;#xa;This role may also be eligible for an Annual Bonus based on a combination of enterprise and individual performance.&amp;#xa;
About Us
Thomson Reuters informs the way forward by bringing together the trusted content and technology that people and organizations need to make the right decisions. We serve professionals across legal, tax, accounting, compliance, government, and media. Our products combine highly specialized software and insights to empower professionals with the data, intelligence, and solutions needed to make informed decisions, and to help institutions in their pursuit of justice, truth, and transparency. Reuters, part of Thomson Reuters, is a world leading provider of trusted journalism and news.
We are powered by the talents of 26,000 employees across more than 70 countries, where everyone has a chance to contribute and grow professionally in flexible work environments. At a time when objectivity, accuracy, fairness, and transparency are under attack, we consider it our duty to pursue them. Sound exciting? Join us and help shape the industries that move society forward.
As a global business, we rely on the unique backgrounds, perspectives, and experiences of all employees to deliver on our business goals. To ensure we can do that, we seek talented, qualified employees in all our operations around the world regardless of race, color, sex/gender, including pregnancy, gender identity and expression, national origin, religion, sexual orientation, disability, age, marital status, citizen status, veteran status, or any other protected classification under applicable law. Thomson Reuters is proud to be an Equal Employment Opportunity Employer providing a drug-free workplace.
Thomson Reuters makes reasonable accommodations for applicants with disabilities, including veterans with disabilities, and for sincerely held religious beliefs in accordance with applicable law. If you reside in the United States and require an accommodation in the recruiting process, you may contact our Human Resources Department at HR.Leave-Expert@thomsonreuters.com . Disability accommodations in the recruiting process may include things like a sign language interpreter, making interview rooms accessible, providing assistive technology, or other relevant accommodations. Please note this email is not intended for general recruitment questions and we will promptly respond to inquiries regarding accommodations. More information on requesting an accommodation here.
Learn more on how to protect yourself from fraudulent job postings here.
More information about Thomson Reuters can be found on thomsonreuters.com</div>

Senior Applied Scientist, NLP/GenAI

<div><h2 class="jobSectionHeader">DESCRIPTION</h2><div>
The Sponsored Products and Brands (SPB) team at Amazon Ads is re-imagining the advertising landscape through state-of-the-art generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising. 
 
The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon&mdash;such as product detail pages, the homepage, and store-in-store pages&mdash;to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack&mdash;from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences&mdash;all designed to deliver meaningful value. 
 
Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we&rsquo;re helping businesses grow on Amazon.com and beyond! 
 
Key job responsibilities 
This role will be pivotal in redesigning how ads contribute to a personalized, relevant, and inspirational shopping experience, with the customer value proposition at the forefront. Key responsibilities include, but are not limited to: 
 
<ul><li>Contribute to the design and development of GenAI, deep learning, multi-objective optimization and/or reinforcement learning empowered solutions to transform ad retrieval, auctions, whole-page relevance, and/or bespoke shopping experiences.</li><li>Collaborate cross-functionally with other scientists, engineers, and product managers to bring scalable, production-ready science solutions to life.</li><li>Stay abreast of industry trends in GenAI, LLMs, and related disciplines, bringing fresh and innovative concepts, ideas, and prototypes to the organization.</li><li>Contribute to the enhancement of team&rsquo;s scientific and technical rigor by identifying and implementing best-in-class algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling.</li><li>Mentor and grow junior scientists and engineers, cultivating a high-performing, collaborative, and intellectually curious team.</li></ul> 
A day in the life 
As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced&mdash;elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon&rsquo;s rich data and the world&rsquo;s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. 
 
About the team 
The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon&mdash;such as product detail pages, the homepage, and store-in-store pages&mdash;to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack&mdash;from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences&mdash;all designed to deliver meaningful value. 
 
Curious about our advertising solutions? Discover more about Sponsored Products and Sponsored Brands to see how we&rsquo;re helping businesses grow on Amazon.com and beyond!</div><h2 class="jobSectionHeader">
BASIC QUALIFICATIONS</h2><ul><li>PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience</li><li>3+ years of building models for business application experience</li><li>Experience programming in Java, C++, Python or related language</li><li>Strong foundation in GenAI, large language models, machine learning, deep learning, probabilistic modeling, and/or optimization.</li><li>Experience developing and deploying models in real-world production environments.</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>Proven expertise in Generative AI, foundation models, LLMs, and/or fine-tuning and customization for downstream tasks.</li><li>Hands-on experience in ads ranking, retrieval, recommendation systems, search, or personalization at web scale.</li><li>Deep understanding of multi-modal modeling, few-shot learning, retrieval-augmented generation (RAG), or reinforcement learning from human feedback (RLHF).</li><li>Experience with online experimentation, A/B testing frameworks, and metrics design for advertising or e-commerce.</li><li>Demonstrated ability to communicate complex technical topics clearly to both technical and non-technical audiences.</li><li>Experience in computational advertising, including familiarity with auction theory, ad economics, and advertiser performance metrics.</li></ul> 
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&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner. 
 
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $136,000/year in our lowest geographic market up to $223,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.</div>

Applied Scientist, Off-Search Relevance, Sponsored Products

<div><h2 class="jobSectionHeader">DESCRIPTION</h2><div>
The Sponsored Products and Brands (SPB) team at Amazon Ads is transforming advertising through generative AI technologies. We help millions of customers discover products and engage with brands across Amazon.com and beyond. Our team combines human creativity with artificial intelligence to reinvent the entire advertising lifecycle&mdash;from ad creation and optimization to performance analysis and customer insights. 
 
We develop responsible AI technologies that balance advertiser needs, enhance shopping experiences, and strengthen the marketplace. Our team values innovation and tackles complex challenges that push the boundaries of what's possible with AI. 
 
Join us in shaping the future of advertising. 
 
Key job responsibilities 
This role will redesign how ads create personalized, relevant shopping experiences with customer value at the forefront. Key responsibilities include: 
 
<ul><li>Design and develop solutions using GenAI, deep learning, multi-objective optimization and/or reinforcement learning to transform ad retrieval, auctions, whole-page relevance, and shopping experiences.</li><li>Partner with scientists, engineers, and product managers to build scalable, production-ready science solutions.</li><li>Apply industry advances in GenAI, Large Language Models (LLMs), and related fields to create innovative prototypes and concepts.</li><li>Improve the team's scientific and technical capabilities by implementing algorithms, methodologies, and infrastructure that enable rapid experimentation and scaling.</li><li>Mentor junior scientists and engineers to build a high-performing, collaborative team.</li></ul> 
A day in the life 
As an Applied Scientist on the Sponsored Products and Brands Off-Search team, you will contribute to the development in Generative AI (GenAI) and Large Language Models (LLMs) to revolutionize our advertising flow, backend optimization, and frontend shopping experiences. This is a rare opportunity to redefine how ads are retrieved, allocated, and/or experienced&mdash;elevating them into personalized, contextually aware, and inspiring components of the customer journey. You will have the opportunity to fundamentally transform areas such as ad retrieval, ad allocation, whole-page relevance, and differentiated recommendations through the lens of GenAI. By building novel generative models grounded in both Amazon&rsquo;s rich data and the world&rsquo;s collective knowledge, your work will shape how customers engage with ads, discover products, and make purchasing decisions. If you are passionate about applying frontier AI to real-world problems with massive scale and impact, this is your opportunity to define the next chapter of advertising science. 
 
About the team 
The Off-Search team within Sponsored Products and Brands (SPB) is focused on building delightful ad experiences across various surfaces beyond Search on Amazon&mdash;such as product detail pages, the homepage, and store-in-store pages&mdash;to drive monetization. Our vision is to deliver highly personalized, context-aware advertising that adapts to individual shopper preferences, scales across diverse page types, remains relevant to seasonal and event-driven moments, and integrates seamlessly with organic recommendations such as new arrivals, basket-building content, and fast-delivery options. To execute this vision, we work in close partnership with Amazon Stores stakeholders to lead the expansion and growth of advertising across Amazon-owned and -operated pages beyond Search. We operate full stack&mdash;from backend ads-retail edge services, ads retrieval, and ad auctions to shopper-facing experiences&mdash;all designed to deliver meaningful value.</div><h2 class="jobSectionHeader">
BASIC QUALIFICATIONS</h2><ul><li>PhD, or Master's degree and 6+ years of applied research experience</li><li>3+ years of building machine learning models for business application experience</li><li>Experience programming in Java, C++, Python or related language</li><li>Strong foundation in GenAI, large language models, machine learning, deep learning, probabilistic modeling, and/or optimization.</li></ul><h2 class="jobSectionHeader">
PREFERRED QUALIFICATIONS</h2><ul><li>Demonstrated expertise in Generative AI technologies, including foundation models, LLMs, and model customization for specific business applications</li><li>Hands-on experience building ads ranking, retrieval, recommendation, or personalization systems that operate at web scale</li><li>Technical proficiency in advanced AI approaches such as multi-modal modeling, few-shot learning, retrieval-augmented generation (RAG), or reinforcement learning from human feedback (RLHF)</li><li>Track record of designing and implementing online experimentation frameworks, including A/B testing methodologies and performance metrics for advertising or e-commerce</li><li>Proven ability to translate complex technical concepts into clear explanations for diverse audiences, from engineers to executives</li><li>Deep knowledge of computational advertising fundamentals, including auction mechanisms, advertising economics, and advertiser success metrics</li></ul> 
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&rsquo;re applying in isn&rsquo;t listed, please contact your Recruiting Partner. 
 
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $150,400/year in our lowest geographic market up to $260,000/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.</div>

Senior Applied Scientist, Off-Search, Sponsored Products and Brands

DataAnnotation

<div>
 We are looking for an Energy Research Scientist to join our team to train AI models. You will measure the progress of these AI chatbots, evaluate their logic, and solve problems to improve the quality of each model.
 
 
 In this role you will need to hold an expert level of physics. Other related fields include, but are not limited to: Physics and/or Applied Mathematics.
 
 
 
 Benefits
 
 
 
 <ul>
 <li>
 This is a full-time or part-time REMOTE position
 </li>
 <li>
 You&rsquo;ll be able to choose which projects you want to work on
 </li>
 <li>
 You can work on your own schedule
 </li>
 <li>
 Projects are paid hourly starting at $40+ USD per hour, with bonuses on high-quality and high-volume work
 
 
 </li>
 </ul>
 
 Responsibilities
 
 
 
 <ul>
 <li>
 Give AI chatbots complex physics problems and evaluate their outputs
 </li>
 <li>
 Evaluate the quality produced by AI models for correctness and performance
 
 
 </li>
 </ul>
 
 Qualifications
 
 
 
 <ul>
 <li>
 Fluency in English (native or bilingual level)
 </li>
 <li>
 Detail-oriented
 </li>
 <li>
 Strong grasp of classical mechanics, E&amp;M, thermodynamics, and basic quantum concepts
 </li>
 <li>
 A current, in progress, or completed Master's and/or PhD is preferred but not required
 
 
 </li>
 </ul>
 
 Notes
 
 
 
 Payment is made via PayPal. We will never ask for any money from you. PayPal will handle any currency conversions from USD. Only applicants in the United States will be considered for this role. This is an independent contract position.
 
 
 #physics
</div>

Energy Research Scientist

Foundational AI Research Scientist - FAIR

Explore 28+ Applied scientist in New York this January. Find roles across top companies and apply now to boost your career.

28 applied scientist jobs IN New York

Applied Scientist, AGI

Senior Applied Scientist, NLP/GenAI

Applied Scientist, Off-Search Relevance, Sponsored Products

Senior Applied Scientist, Off-Search, Sponsored Products and Brands

Energy Research Scientist

Energy Research Scientist

Foundational AI Research Scientist - FAIR