Are We Strategically Naive or Guided by Trust and Trustworthiness in Cheap-Talk Communication.png
Are We Strategically Naive or Guided by Trust and Trustworthiness in Cheap-Talk Communication?” was published in Management Science — the flagship journal of the Institute for Operations Research and the Management Sciences (INFORMS) in April 2021.
Glynis Condon

3 questions with Özalp Özer: How to build trust in business relationships

Özer’s paper published in INFORMS’ Management Science 2021 explores the dynamics behind “cheap-talk” communications.

Trust and trustworthiness are important in both our personal and business relationships. How then can we build environments that foster increased trust, trustworthiness and cooperation?

In the first edition of a new series that focuses on research papers published by scientists within the Amazon Supply Chain Optimization Technologies (SCOT) organization, we interview Özalp Özer, coauthor of “Are We Strategically Naive or Guided by Trust and Trustworthiness in Cheap-Talk Communication?”. The paper was published in Management Science — the flagship journal of the Institute for Operations Research and the Management Sciences (INFORMS) in April 2021.

Özalp Özer profile image
Özalp Özer is a senior principal scientist at Amazon, and George and Fonsa Brody Professor of Management Science at The University of Texas at Dallas.

Özer is a senior principal scientist at Amazon, and George and Fonsa Brody Professor of Management Science at The University of Texas at Dallas (UTD). He earned a PhD in operations research from Columbia University, before going on to serve on the faculty at Stanford and Columbia. Özer has published extensively on a diverse range of topics, from supply chain management, capacity and inventory management to pricing and revenue management.

Özer says that a guiding principle behind his research is to focus on solving problems that have a real-world impact at scale. At Stanford and then UTD, Özer found himself drawn to the field of behavioral and experimental economics — particularly the field of game theory and understanding how to model actions and emotions in scenarios involving multiple decision makers in dynamic environments.

Driven by his interest in tackling real-world business problems, Özer remained engaged with industry during his tenure as an academic. While working on a project focused on designing effective procurement contracts, he observed the important role that trust played in establishing and fostering business relationships.

In many cases, the interests of the parties engaging in a negotiation are not aligned. To give one example, suppliers can use product forecast information from a buyer to make capacity, inventory and other manufacturing-related decisions. However, buyers might often provide suppliers with overly optimistic forecasts to ensure an abundant supply. If the demand for the product turns out to be lower than anticipated, the supplier bears the excess investment risk.

Özer says that this scenario represents an example of “cheap talk communications.” He outlines three characteristics that are common to all cheap talk communications: they are costless (they are devoid of monetary penalties), they are non-binding (a buyer can provide a forecast without committing to it), and they are non-verifiable (no forecast can be completely accurate in the light of market uncertainty). To complicate matters, the objective functions that each party is trying to maximize are at odds (or not perfectly aligned) with each other.

Standard game theory suggests that each party in a business transaction will move toward an equilibrium that maximizes their own payoff. In a cheap-talk setting, where the information is costless, non-binding and non-verifiable, the theory suggests that each party will disregard the information supplied by the other.

However, Özer finds that people involved in business (as well as personal) transactions frequently factor into their decision-making information supplied by the other party, even when their incentives are not perfectly aligned and even when the information or recommendation may be perceived as “cheap”. They do this by taking the business context and the related relationship into account. Doing so results in higher returns for both parties involved. For example, third-party sellers are more likely to act on price reduction or replenishment recommendations from Amazon, if they find that these recommendations have previously resulted in an uptick in sales and profits.

Ozer says that “cheap talk” communications have the unfortunate emphasis on being “cheap” and less emphasis on how they are informative and can align incentives. In a series of publications, Ozer shows why, when, and how such communications and recommendations turn out to be informative, and how they help align business objectives, resulting in both parties making better decisions.   

In this interview, Özer talks about findings from the recently published INFORMS paper and discusses the implications of these findings for companies like Amazon.

Q. What are the two models that can be used to explain how cheap talk communications work between decision makers?

As our paper suggests, there are two contrasting economic theories that can be used to analyze cheap-talk communications.

The trust-embedded model — which takes a more optimistic view of humanity — suggests that decision makers are motivated by non-monetary motives to be trusting and trustworthy, besides the monetary incentives such as maximizing cash flow.   

Here, we define trust as instances of decision makers behaving voluntarily in a way that put themselves in vulnerable engagement due to the uncertain behavior of the other party (the trustee), based upon the expectation of a positive outcome from that engagement. Trustworthiness flips the perspective to that of the trustee. We define trustworthiness as an instance of a decision maker behaving voluntarily in a way not to take advantage of the trustor’s vulnerable position – even when faced with a self-serving decision that conflicts with the trustor’s objectives.

Humans use non-Bayesian, trust-based belief systems to update their rules governing interactions with other parties. In short, people involved in a business transaction are willing to be vulnerable and take risk.
Özalp Özer

The trust-embedded model suggests that when engaging with others, decision makers are averse to manipulating information in economic interactions. They incur disutility from lying. As a result, they assess the trustworthiness of the counterparty, and they form a trust factor towards them. This trust factor governs how decision makers interpret and use the information they receive from others.

In other words, humans use non-Bayesian, trust-based belief systems to update their rules governing interactions with other parties. In short, people involved in a business transaction are willing to be vulnerable and take risk. Because they assess — even sometimes incorrectly — that doing so yields positive outcomes, they engage in and cultivate behaviors conducive to enabling these outcomes.

The trust embedded model suggests that individuals are guided by more than self-interest or pecuniary motives as they engage in transactions. For example, senders of information are guided by factors such as fairness and tenets that are central to their company. As a result, they share more information and resources than strictly necessary.

In contrast to the trust-embedded model, the level-k model — the second model discussed in the paper — suggests that decision makers are limited in their ability to think strategically. Receivers of information cannot anticipate the extent to which the sender might have distorted the message. On the flip-side, senders cannot account for just how much receivers might discount their message. Consequently, senders share more than necessary, because they take a dim view of the receiver’s ability to discount their message.

It’s important to note that even the level-k model can sometimes explain why senders and receivers tend to overshare information in a cheap-talk setting, which contrasts with the outcome standard game theory models would predict. It’s just that their motivations are different – with the level-k model, oversharing is driven by a limited ability to think strategically, rather than by the willingness to be trusting and trustworthy.

Overall, our paper that analyzed existing cheap-talk experiment data, found more support for the trust-embedded model, suggesting that individuals are also driven by non-monetary incentives when conducting transactions.

Q. Why do you think that trust-embedded models do a better job of explaining cheap-talk communications? What are the implications for organizations engaging in relationships with businesses and partners?

During the internet age, we’ve seen e-commerce, hospitality and ride-sharing companies grow precisely because they’ve been able to create policies and tools that encourage trust.
Özalp Özer

The answer to your first question is relatively simple — human beings are far more sophisticated than the level-k model gives them credit for. For example, there are many sellers on Amazon’s website who are proficient in using a variety of tools they have developed to make decisions related to pricing and inventory.

As a result, if we want the tools we provide to earn sellers’ trust, we need to think of the system more holistically at both an architecture and policy level to truly understand what builds trust and what is a trust-buster.

During the internet age, we’ve seen e-commerce, hospitality and ride-sharing companies grow precisely because they’ve been able to create policies and tools that encourage trust. Product reviews, the ability to get refunds for a vacation rental because hosts might not have lived up to their promises, or the price for a ride being set in advance — these are some of the mechanisms that let you buy a product or rent a home from people you don’t know.

Q. How are the findings in your paper applicable to your work at Amazon?

We are leveraging the insights from this stream of research as well as others to augment our understanding of seller trust, particularly in relation to how sellers interact with our inventory management tools, and how fidelity of recommendations impact sellers’ trust.

There is no interaction at Amazon that I can think of that doesn’t have an element of trust.
Özalp Özer

We are designing our related processes to reduce barriers for trusting and trustworthy engagements among the participants of our stores; for example, by making specific investments to support seller growth in areas that benefit sellers and customers the most; by reducing perceived vulnerabilities in carrying excess inventory; by looking into ways in which we stabilize our policies; by creating visibility to the reasons for our recommendations; by looking into ways in which we can build interactive communication channels among participants in our stores; and by building reputation and feedback systems that foster trusting and trustworthy engagements and on and on.

Using large-scale data, scientific methods like causal machine learning to optimization, as well as continual engagement with selling partners and customers, we aim to identify at the extent to which sellers trust evolves — so we can identify and invest in processes that foster trust and as a result growth and economic prosperity.  

There is no interaction at Amazon that I can think of that doesn’t have an element of trust. Jeff Bezos has said, “You can’t ask for trust, you just have to do it the hard way, one step at a time.” In my time at the company, I have been struck by the tireless efforts of so many people to gain seller and customer trust. At Amazon, it is just part of everything we do.

Related content

US, MA, North Reading
We are looking for experienced scientists and engineers to explore new ideas, invent new approaches, and develop new solutions in the areas of Controls, Dynamic modeling and System identification. Are you inspired by invention? Is problem solving through teamwork in your DNA? Do you like the idea of seeing how your work impacts the bigger picture? Answer yes to any of these and you’ll fit right in here at Amazon Robotics. We are a smart team of doers that work passionately to apply cutting edge advances in robotics and software to solve real-world challenges that will transform our customers’ experiences in ways we can’t even imagine yet. We invent new improvements every day. We are Amazon Robotics and we will give you the tools and support you need to invent with us in ways that are rewarding, fulfilling and fun. Key job responsibilities Applied Scientists take on big unanswered questions and guide development team to state-of-the-art solutions. We want to hear from you if you have deep industry experience in the Mechatronics domain and : * the ability to think big and conceive of new ideas and novel solutions; * the insight to correctly identify those worth exploring; * the hands-on skills to quickly develop proofs-of-concept; * the rigor to conduct careful experimental evaluations; * the discipline to fast-fail when data refutes theory; * and the fortitude to continue exploring until your solution is found We are open to hiring candidates to work out of one of the following locations: North Reading, MA, USA | Westborough, MA, USA
GB, London
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Some knowledge of econometrics, as well as basic familiarity with Python or R is necessary, and experience with SQL and UNIX would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at We are open to hiring candidates to work out of one of the following locations: London, GBR
IN, KA, Bengaluru
Job Description ATE (Analytics, Technology and Engineering) is a multi-disciplinary team of scientists, engineers, and technicians, all working to innovate in operations for the benefit of our customers. Our team is responsible for creating core analytics, science capabilities, platforms development and data engineering. We develop scalable analytics applications and research modeling to optimize operation processes.. You will work with professional software development managers, data engineers, data scientists, applied scientists, business intelligence engineers and product managers using rigorous quantitative approaches to ensure high quality data tech products for our customers around the world, including India, Australia, Brazil, Mexico, Singapore and Middle East. We are on the lookout for an enthusiastic and highly analytical individual to be a part of our journey. Amazon is growing rapidly and because we are driven by faster delivery to customers, a more efficient supply chain network, and lower cost of operations, our main focus is in the development of strategic models and automation tools fed by our massive amounts of available data. You will be responsible for building these models/tools that improve the economics of Amazon’s worldwide fulfillment networks in emerging countries as Amazon increases the speed and decreases the cost to deliver products to customers. You will identify and evaluate opportunities to reduce variable costs by improving fulfillment center processes, transportation operations and scheduling, and the execution to operational plans. You will also improve the efficiency of capital investment by helping the fulfillment centers to improve storage utilization and the effective use of automation. Finally, you will help create the metrics to quantify improvements to the fulfillment costs (e.g., transportation and labor costs) resulting from the application of these optimization models and tools. Major responsibilities include: · In this role, you will be responsible for developing and implementing innovative, scalable models and tools aimed at tackling novel challenges within Amazon’s global fulfillment network. Collaborating with fellow scientists from various teams, you will work on integrated solutions to enhance fulfillment speed, reduce costs. Your in-depth comprehension of business challenges will enable you to provide scientific analyses that underpin critical business decisions, utilizing a diverse range of methodologies. You’ll have the opportunity to design scientific tool platforms, deploy models, create efficient data pipelines, and streamline existing processes. Join us in shaping the future of Amazon’s global retail business by optimizing delivery speed at scale and making a lasting impact on the world of e-commerce. If you’re passionate about solving complex problems and driving innovation, we encourage you to apply. About the team This team is responsible for applying science based algo and techniques to solve the problems in operation and supply chain. Some of these problems include, volume forecasting, capacity planning, fraud detection, scenario simulation and using LLM/GenAI for process efficiency We are open to hiring candidates to work out of one of the following locations: Bengaluru, KA, IND
IL, Tel Aviv
Are you passionate about pushing the boundaries of computer vision, generative AI, deep learning, and machine learning? Ready to tackle challenges in document understanding at scale? We’re looking for innovative minds to join our world-class team at AWS, where you’ll collaborate with leading researchers, academics, and engineers on Amazon Textract. Why AWS? Be part of the leading cloud service provider powering innovation and positive impact. Work on real-world problems alongside tech and business giants. Access to unlimited data and computational resources. Collaborate with world-class researchers and developers. Deploy solutions at AWS scale and publish your work at top conferences. Focus Areas: - LLMs, document understanding, scene text recognition. - Visual question answering, NLP+vision, layout understanding. Locations: Tel Aviv and Haifa Think you’re a fit? Dive into the world of AWS Computer Vision and help us innovate at the forefront of technology. Key job responsibilities - Design cutting-edge neural network architectures. - Create document understanding solutions for complex scenarios and large visual datasets. - Set benchmarks and success criteria for model performance. - Collaborate across AWS and Amazon to bring scientific breakthroughs to our customers. - Add your unique creativity to our multidisciplinary team. - Mentor junior scientists and interns/PhD students. We are open to hiring candidates to work out of one of the following locations: Haifa, ISR | Tel Aviv, ISR
LU, Luxembourg
Have you ever wished to build high standard Operations Research and Machine Learning algorithms to optimize one of the most complex logistics network? Have you ever ordered a product on Amazon websites and wondered how it got delivered to you so fast, and what kinds of algorithms & processes are running behind the scenes to power the whole operation? If so, this role is for you. The team: Global transportation services, Research and applied science - Operations is at the heart of the Amazon customer experience. Each action we undertake is on behalf of our customers, as surpassing their expectations is our passion. We improve customer experience through continuously optimizing the complex movements of goods from vendors to customers throughout Europe. - Global transportation analytical teams are transversal centers of expertise, composed of engineers, analysts, scientists, technical program managers and developers. We are focused on Amazon most complex problems, processes and decisions. We work with fulfillment centers, transportation, software developers, finance and retail teams across the world, to improve our logistic infrastructure and algorithms. - GTS RAS is one of those Global transportation scientific team. We are obsessed by delivering state of the art OR and ML tools to support the rethinking of our advanced end-to-end supply chain. Our overall mission is simple: we want to implement the best logistics network, so Amazon can be the place where our customers can be delivered the next-day. The role: Applied scientist, speed and long term network design The person in this role will have end-to-end ownership on augmenting RAS Operation Research and Machine Learning modeling tools. They will help understand where are the constraints in our transportation network, and how we can remove them to make faster deliveries at a lower cost. You will be responsible for designing and implementing state-of-the-art algorithmic in transportation planning and network design, to expand the scope of our Operations Research and Machine Learning tools, to reflect the constantly evolving constraints in our network. You will enable the creation of a product that drives ever-greater automation, scalability and optimization of every aspect of transportation, planning the best network and modeling the constraints that prevent us from offering more speed to our customer, to maximize the utilization of the associated resources. The impact of your work will be in the Amazon EU global network. The product you will build will span across multiple organizations that play a role in Amazon’s operations and transportation and the shopping experience we deliver to customer. Those stakeholders include fulfilment operations and transportation teams; scientists and developers, and product managers. You will understand those teams constraints, to include them in your product; you will discuss with technical teams across the organization to understand the existing tools and assess the opportunity to integrate them in your product.You will engage with fellow scientists across the globe, to discuss the solutions they have implemented and share your peculiar expertise with them. This is a critical role and will require an aptitude for independent initiative and the ability to drive innovation in transportation planning and network design. Successful candidates should be able to design and implement high quality algorithm solutions, using state-of-the art Operations Research and Machine Learning techniques. Key job responsibilities - Engage with stakeholders to understand what prevents them to build a better transportation network for Amazon - Review literature to identify similar problems, or new solving techniques - Build the mathematical model representing your problem - Implement light version of the model, to gather early feed-back from your stakeholders and fellow scientists - Implement the final product, leveraging the highest development standards - Share your work in internal and external conferences - Train on the newest techniques available in your field, to ensure the team stays at the highest bar About the team GTS Research and Applied Science is a team of scientists and engineers whom mission is to build the best decision support tools for strategic decisions. We model and optimize Amazon end-to-end operations. The team is composed of enthusiastic members, that love to discuss any scientific problem, foster new ideas and think out of the box. We are eager to support each others and share our unique knowledge to our colleagues. We are open to hiring candidates to work out of one of the following locations: Luxembourg, LUX
US, CA, Santa Clara
Amazon AI is looking for world class scientists and engineers to join its AWS AI Labs. This group is entrusted with developing core data mining, natural language processing, deep learning, and machine learning algorithms for AWS. You will invent, implement, and deploy state of the art machine learning algorithms and systems. You will build prototypes and explore conceptually new solutions. You will interact closely with our customers and with the academic community. You will be at the heart of a growing and exciting focus area for AWS and work with other acclaimed engineers and world famous scientists. Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. We are open to hiring candidates to work out of one of the following locations: New York, NY, USA | Santa Clara, CA, USA | Seattle, WA, USA
DE, BE, Berlin
Are you excited about developing state-of-the-art computer vision models that revolutionize Amazon’s Fulfillment network? Are you looking for opportunities to apply AI on real-world problems at truly vast scale? At Amazon Fulfillment Technologies and Robotics, we are on a mission to build high-performance autonomous systems that perceive and act to further improve our world-class customer experience — at Amazon scale. To this end, we are looking for an Applied Scientist who will build and deploy models that make smarter decisions on a wide array of multi-modal signals. Together, we will be pushing beyond the state of the art in optimizing one of the most complex systems in the world: Amazon's Fulfillment Network. Key job responsibilities In this role, you will build computer vision and multi-modal deep learning models that understand the state of products and packages flowing through Amazon’s fulfillment network. You will build models that solve challenging problems like product identification and damage detection on Amazon's entire retail catalog (billions of different items, thousands of new items every day). You will primarily work with very large real-world vision datasets, as well as a diverse set of multi-modal datasets, including natural language and structured data. You will face a high level of research ambiguity and problems that require creative, ambitious, and inventive solutions. A day in the life AFT AI delivers the AI solutions that empower Amazon’s fulfillment network to make smarter decisions. You will work on an interdisciplinary team of scientists and engineers with deep expertise in developing cutting-edge AI solutions at scale. You will work with images, videos, natural language, and sequences of events from existing or new hardware. You will adapt state-of-the-art machine learning and computer vision techniques to develop solutions for business problems in the Amazon Fulfillment Network. About the team Amazon Fulfillment Technologies (AFT) powers Amazon’s global fulfillment network. We invent and deliver software, hardware, and science solutions that orchestrate processes, robots, machines, and people. We harmonize the physical and virtual world so Amazon customers can get what they want, when they want it. AFT AI is spread across multiple locations in NA (Bellevue WA and Nashville, TN) and Europe (Berlin, Germany). We are hiring candidates to work out of the Berlin location. Publicly available articles showcasing some of our work: - Damage Detection: - Product ID: We are open to hiring candidates to work out of one of the following locations: Berlin, BE, DEU
US, WA, Seattle
Here at Amazon, we embrace our differences. We are committed to furthering our culture of diversity and inclusion of our teams within the organization. How do you get items to customers quickly, cost-effectively, and—most importantly—safely, in less than an hour? And how do you do it in a way that can scale? Our teams of hundreds of scientists, engineers, aerospace professionals, and futurists have been working hard to do just that! We are delivering to customers, and are excited for what’s to come. Check out more information about Prime Air on the About Amazon blog ( If you are seeking an iterative environment where you can drive innovation, apply state-of-the-art technologies to solve real world delivery challenges, and provide benefits to customers, Prime Air is the place for you. Come work on the Amazon Prime Air Team! Prime Air is seeking an experienced Research Scientist in the Flight Sciences High-Fidelity Methods (HFM) team within Flight Sciences, you will develop and verify aerodynamics models used for engineering analyses and vehicle simulation. These models are the backbone of every flight simulation performed within Prime Air and are a critical element in the aircraft design, verification and certification process. These models are used to predict many attributes of the vehicle performance including range, maneuverability, tracking error, and aircraft stability. They are a key input to design decisions, vehicle component sizing and flight software algorithm development. The accuracy and reliability of these flight model are critical to the success of Prime Air. For this role we are looking for a scientist to develop surrogate or machine learning models to represent the complex aerodynamic behavior of our drones. This scientist will develop techniques to validate these models using flight testing, quantify the model uncertainty, and assess the impact of this uncertainty on downstream engineering analyses. Key job responsibilities A Research Scientist in this role is responsible for owning the development, deployment, verification, and maintenance of models from end-to-end. This includes the initial gathering of the downstream customer needs, identifying the most suitable modelling approach, coordinating the generation of input data, training models, developing and maintaining software interfaces, and verifying the model accuracy. A Research Scientist in this role is responsible for determining the most suitable modeling approach for a given physical phenomena. They need to possess knowledge of various machine learning techniques, and their respective advantages and limitations. They will need to have a detailed understanding of the types of physics to be modelled including vehicle aerodynamics, multibody dynamics, and atmosphere physics. This role is responsible for designing experiments for generating data used to train and verify surrogate models. They need to have a basic understanding of the methods used to generate high-fidelity aerodynamics predictions including CFD, wind tunnel testing, and flight testing. They will be responsible for validating the models by leveraging uncertainty quantification, system identification, and statical analyses. Export Control License This position may require a deemed export control license for compliance with applicable laws and regulations. Placement is contingent on Amazon’s ability to apply for and obtain an export control license on your behalf. A day in the life A Research Scientist in the High-Fidelity Methods (HFM) team will have the opportunity to work on a wide variety of tasks. The ideal candidate should be adaptable and thrive in an everchanging environment. Depending on the phase of model or vehicle development, a typical day might consist of reading research papers on machine learning techniques, developing test plans for wind tunnel testing, writing code to train and verify models, reviewing flight test results, or writing documentation. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Seattle
Innovators wanted! Are you an entrepreneur? A builder? A dreamer? This role is part of an Amazon Special Projects team that takes the company’s Think Big leadership principle to the extreme. We focus on creating entirely new products and services with a goal of positively impacting the lives of our customers. No industries or subject areas are out of bounds. If you’re interested in innovating at scale to address big challenges in the world, this is the team for you. Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have thirteen employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We are constantly learning through programs that are local, regional, and global. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust. As a Applied Scientist at the intersection of machine learning and the life sciences, you will participate in developing exciting products for customers. Our team rewards curiosity while maintaining a laser-focus in bringing products to market. Competitive candidates are responsive, flexible, and able to succeed within an open, collaborative, entrepreneurial, startup-like environment. At the cutting edge of both academic and applied research in this product area, you have the opportunity to work together with a diverse and talented team of scientists, engineers, and product managers and collaborate with others teams. We are open to hiring candidates to work out of one of the following locations: Seattle, WA, USA
US, WA, Bellevue
As a Principal Research Scientist in the Amazon Artificial General Intelligence (AGI) Data Services organization, you will be responsible for sourcing and quality of massive datasets powering Amazon's AI. You will play a critical role in driving innovation and advancing the state-of-the-art in natural language processing and machine learning. You will be responsible for developing and implementing cutting-edge algorithms and techniques to extract valuable insights from large-scale data sources. You will work closely with cross-functional teams, including product managers, engineers, and data scientists to ensure that our AI systems are aligned with human policies and preferences. Key job responsibilities - Responsible for sourcing and quality of massive datasets powering Amazon's AI. - Collaborate with cross-functional teams to ensure that Amazon’s AI models are aligned with human preferences. - Develop and implement strategies to improve the efficiency and effectiveness of programs delivering massive datasets. - Identify and prioritize research opportunities that have the potential to significantly impact our AI systems. - Communicate research findings and progress to senior leadership and stakeholders. We are open to hiring candidates to work out of one of the following locations: Bellevue, WA, USA | Boston, MA, USA