Nia Jetter, senior principal technologist for Amazon Fulfillment Technology Robotics, is seen speaking into a mic near a stand with an open laptop on it
Nia Jetter, senior principal technologist for Amazon Fulfillment Technology Robotics, is working on improving components of Amazon’s delivery operations by focusing on embedding best practices into the design process.

From aerospace to Amazon, Nia Jetter is blazing new paths

Jetter says her goals include lowering barriers to understanding technology and cultivating a more diverse workforce.

“I work in robotics and artificial intelligence. We're building robots that are going to help the world.” As introductions — or elevator pitches — go, that’s an especially strong one.

That’s how Nia Jetter, senior principal technologist for Amazon Fulfillment Technology Robotics, answers the question: What do you do?

Jetter is an engineer who has been recognized throughout her career for her accomplishments in autonomous systems, so her confidence is earned. Her goals extend beyond developing new algorithms, and include lowering barriers to understanding technology and cultivating a more diverse workforce.

“At Amazon, I am working on laying a foundation for how we build collaborative autonomous systems safely across our robotics platforms,” Jetter notes. “I’m also working on forward looking research on ways to architect and develop safety critical autonomous systems in a way that is verifiable while leveraging techniques like machine learning.”

Jetter’s work is centered on improving components of Amazon’s delivery operations by focusing on embedding best practices into the design process. She believes automation, achieved with artificial intelligence and next-generation robots, can deliver improvements for both Amazon employees and customers.

Robotics research at Amazon
Company is testing a new class of robots that use artificial intelligence and computer vision to move freely throughout facilities.

“People want their packages quickly, including me,” she says with a laugh. “So, when you look at our fulfillment centers, I'm hugely passionate about: What are the ways that we can help my colleagues working there? How can we help our customers?”

To that end, Jetter, along with other scientists and engineers within her organization, is analyzing activities that could be more easily and safely accomplished with robots. In order to support this work, her team and others across Amazon collaborate with a variety of universities, including the University of Washington. Jetter sits on the advisory board for the UW-Amazon Science Hub and also serves as an Amazon research liaison.

“We are working on developing solutions to challenges faced across multiple industries and are working to do so in a scalable fashion by developing in a way that supports modularity. There is a lot of space for innovation in safe autonomy, AI, and robotics,” she said. “I am passionate about pursuing research that can be inserted into products in that space.”

An early love for learning

Jetter displayed engineering talent from a young age.

As a second grader she would find scrap insulated wire at the base of utility poles, and would save quarters given to her by her grandfather for small chores to buy LEDs, light bulbs, and batteries from RadioShack. Her father, a mail carrier, would help her find books that explained electrical circuits. While in elementary school, she used a piece of foam core and her RadioShack purchases to create an illuminated Valentine’s Day card for her science teacher.

Her path shifted toward computer programming while she was still in elementary school. She took a computer class and said her interest was immediately piqued. She began spending her spare moments in the computer room writing programs in HyperCard, soon followed by Fortran, Pascal, and C.

“I loved programming at school,” she says. “I would go on my lunch hour and stay after school. Looking back, while at the time I did not think of it as something I would do for a career, I realize I was good at it.”

In high school, she received a letter from MIT encouraging her to apply to the MIT Introduction to Technology, Engineering, and Science (MITES) program. At the time, the program took 50 high school students and brought them to campus to take intense science and engineering classes and to familiarize them with the institute.

He didn’t see me as a black girl who was good at math. He saw me as a mathematician. That meant the world to me.
Nia Jetter

Jetter said the magnitude of the potentially life-changing opportunity was not immediately evident to her, namely because she had never heard of MIT. “Little did I realize that that letter, and attending the MITES program, would become a significant part of my origin story as an engineer,” she noted.

Her experience with MITES led directly to enrolling at MIT. She intended to study biochemical engineering, but while there she was exposed to more advanced math and computer science classes and found that she loved them. Her career path was set when, in her sophomore year, she took an artificial intelligence class with the late Patrick Henry Winston, her future mentor and then director of the MIT Artificial Intelligence Laboratory.

“There are several points in my journey where I met people who saw more in me than I saw in myself, people who filled a gap for me through exposure to what was possible. Professor Winston saw me as a scientist and a mathematician first, and encouraged me to push the envelope and be all that I could be.

Nia Jetter is seen sitting in a chair with a telescope on a stand in front of her and windows behind her, she is smiling into the camera
Nia Jetter said her career path was set when she took an artificial intelligence class with the late Patrick Henry Winston. “Professor Winston saw me as a scientist and a mathematician first, and encouraged me to push the envelope and be all that I could be."

“He didn’t see me as a black girl who was good at math. He saw me as a mathematician. That meant the world to me,” Jetter says.

A lifelong science fiction fan, Jetter also set her sights on working for NASA. She interned there for three summers.

“When I was on the atmospheric experiments team, I recognized that their algorithms could be improved. I’m not sure they took the suggestion from an intern seriously, but I wrote a paper explaining what I saw, and I gave it to the department head,” she recalled. “The next Monday, he came into the office and told me to get started.

“What I learned from my NASA internships was the value of being a mathematician or a computer scientist. I learned that every team needs a computer scientist.”

Before her graduation from MIT in 2000, a chance encounter with a recruiter from Hughes Space and Communications (acquired in October 2000 by Boeing) convinced her to work there on a project involving automated controls. Although she had some early challenges, she quickly realized she could solve those by drawing on her own experiences.

“I derived mathematical models and eventually I was asked to ‘Derive the gains for the controller.’ At the time I had no idea what that meant. I was fortunate to be taught by leaders in the field and quickly learned that a controller is very analogous to an intelligent agent in how it needs to perceive, make decisions and act on its environment. That work led me to enroll at Stanford in 2005 to get a master’s degree in aeronautical and astronautical engineering while I worked at Boeing.”

A milestone moment

In 2013, her work at Boeing led to her being honored as a Boeing associate technical fellow – the first tier of the Technical Fellowship. At the Boeing facility in El Segundo, California, in what is called the “hall of flags,” there is a wall with photos of the Boeing technical fellows.

“From when I first saw the wall, I knew that one day my face would be on it. I’ll never forget the day I walked down the hall and my photo was up! I was the first black woman with my face on the wall at my site. I didn’t realize the photo would mean so much to me, but when I first saw it on the wall, it really stood out.”

Diversity, equity, and inclusion
Program is aimed at expanding participation in operations research, management science, and analytics research for those from underrepresented backgrounds.

In 2020, Jetter made what she admitted was a hard decision. “I decided to leave aerospace in order to be able to innovate faster and to see the fruits of innovation sooner.” She knew that kind of opportunity existed at Amazon, and joined the company in January 2021 to work with the robotics team.

“While I thought that I was making a decision to leave aerospace, I was actually making a decision to expand my expertise in autonomy and AI. So much of the work that I do now is enabled by my aerospace foundation. What excites me about robotics and artificial intelligence at Amazon is the opportunity to truly change the game, change how we do things for an additional set of customers,” Jetter said.

Blazing a trail

As a leader in AI and robotics, Jetter says many people approach her with interest in pursuing a similar path, asking whether they can emulate her. Many of those who approach her have what is for her a familiar experience: a lack of exposure.

“This has inspired me because I am often approached by people who clearly have the aptitude but have not been exposed to a mechanism — including tools they need to progress down the path. Sometimes they just need exposure to people who look like them going down the path. As a result, in addition to building a solid tech foundation, when mentoring I focus on exposure, encouragement, and helping people see things that they might not see in themselves.”

That’s also why diversity matters for human beings solving complex science and engineering problems. If you have diverse perspectives in the room, you can arrive at the optimal solution for the target customer faster.
Nia Jetter

To lower the barriers to entry, Jetter makes time to provide guidance to others. She does this in a number of ways, including small group mentoring sessions that she calls “Shades of Tech”. In addition, earlier this year Jetter spearheaded the Amazon in the City Responsible AI Panel with support from Amazon’s Inclusive Experiences and Technology team. The event brought together “leaders from within and outside Amazon to share perspectives on the importance of fairness in tech as AI-based technology is developed and deployed.”

Along with Jetter, attendees heard from Nashlie Sephus, principal AI/ML evangelist with Amazon Web Services; Chad Jenkins, associate chair of undergraduate studies and professor of robotics at the University of Michigan; and Nii Simmonds, non-resident fellow at the Center For Global Development. The panelists spoke about responsible AI and the impact of diversity in the workforce.

Jetter drew on her own past experiences when pondering the initiative.

“There are certain types of optimization algorithms where, when you're optimizing, you get to a point at which you're actually converging on a local solution, as opposed to the global solution. And in order to get to the global solution, you actually need to inject variety – you have to inject diversity in your dataset.

“That’s also why diversity matters for human beings solving complex science and engineering problems. If you have diverse perspectives in the room, you can arrive at the optimal solution for the target customer faster.”

What is artificial intelligence?

In another effort to expand access, Jetter created a series of YouTube videos explaining automation and artificial intelligence called “Thinque Bytes.”

“I feel very fortunate to be where I am today. I want to provide exposure to enable as many people as possible who might not have easy access to the knowledge and the technology to learn and eventually have impact in these fields.”

Research areas

Related content

IN, HR, Gurugram
We're on a journey to build something new a green field project! Come join our team and build new discovery and shopping products that connect customers with their vehicle of choice. We're looking for a talented Senior Applied Scientist to join our team of product managers, designers, and engineers to design, and build innovative automotive-shopping experiences for our customers. This is a great opportunity for an experienced engineer to design and implement the technology for a new Amazon business. We are looking for a Applied Scientist to design, implement and deliver end-to-end solutions. We are seeking passionate, hands-on, experienced and seasoned Senior Applied Scientist who will be deep in code and algorithms; who are technically strong in building scalable computer vision machine learning systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation.. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and dynamic learning. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on complex customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a unified team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a bold disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement Machine Learning models for vision systems on robotic platforms Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. Work with stakeholders across , science, and operations teams to iterate on design and implementation. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment Work directly with customers and partners to test prototypes and incorporate feedback Mentor other engineer team members. A day in the life - 6+ years of building machine learning models for retail application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning - Demonstrated expertise in computer vision and machine learning techniques.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field or relevant science experience (publications/scientific prototypes) in lieu of Masters - Experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment - Papers published in AI/ML venues of repute
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
US, WA, Seattle
The Search Supply & Experiences team, within Sponsored Products, is seeking an Applied Scientist to solve challenging problems in natural language understanding, personalization, and other areas using the latest techniques in machine learning. In our team, you will have the opportunity to create new ads experiences that elevate the shopping experience for our hundreds of millions customers worldwide. As an Applied Scientist, you will partner with other talented scientists and engineers to design, train, test, and deploy machine learning models. You will be responsible for translating business and engineering requirements into deliverables, and performing detailed experiment analysis to determine how shoppers and advertisers are responding to your changes. We are looking for candidates who thrive in an exciting, fast-paced environment and who have a strong personal interest in learning, researching, and creating new technologies with high customer impact. Key job responsibilities As an Applied Scientist on the Search Supply & Experiences team you will: - Perform hands-on analysis and modeling of enormous datasets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, and complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Design and run experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Stay up to date on the latest advances in machine learning. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to shoppers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
US, WA, Seattle
Amazon.com strives to be Earth's most customer-centric company where customers can shop in our stores to find and discover anything they want to buy. We hire the world's brightest minds, offering them a fast paced, technologically sophisticated and friendly work environment. Economists at Amazon partner closely with senior management, business stakeholders, scientist and engineers, and economist leadership to solve key business problems ranging from Amazon Web Services, Kindle, Prime, inventory planning, international retail, third party merchants, search, pricing, labor and employment planning, effective benefits (health, retirement, etc.) and beyond. Amazon Economists build econometric models using our world class data systems and apply approaches from a variety of skillsets – applied macro/time series, applied micro, econometric theory, empirical IO, empirical health, labor, public economics and related fields are all highly valued skillsets at Amazon. You will work in a fast moving environment to solve business problems as a member of either a cross-functional team embedded within a business unit or a central science and economics organization. You will be expected to develop techniques that apply econometrics to large data sets, address quantitative problems, and contribute to the design of automated systems around the company. About the team The International Seller Services (ISS) Economics team is a dynamic group at the forefront of shaping Amazon's global seller ecosystem. As part of ISS, we drive innovation and growth through sophisticated economic analysis and data-driven insights. Our mission is critical: we're transforming how Amazon empowers millions of international sellers to succeed in the digital marketplace. Our team stands at the intersection of innovative technology and practical business solutions. We're leading Amazon's transformation in seller services through work with Large Language Models (LLMs) and generative AI, while tackling fundamental questions about seller growth, marketplace dynamics, and operational efficiency. What sets us apart is our unique blend of rigorous economic methodology and practical business impact. We're not just analyzing data – we're building the frameworks and measurement systems that will define the future of Amazon's seller services. Whether we're optimizing the seller journey, evaluating new technologies, or designing innovative service models, our team transforms complex economic challenges into actionable insights that drive real-world results. Join us in shaping how millions of businesses worldwide succeed on Amazon's marketplace, while working on problems that combine economic theory, advanced analytics, and innovative technology.