A grid of 12 women scientists who were asked what three steps we can take as a society to forge a more gender-equal science community
As International Women's Day approached, we asked women scientists from research areas across the company what three steps we can take as a society to forge a more gender-equal science community.
Credit: Glynis Condon

How to forge a more gender-equal science community

International Women's Day is March 8 with the theme: #ChooseToChallenge.

International Women’s Day (IWD) is March 8, 2021. The day celebrates the social, economic, cultural and political achievements of women, and also denotes a call to action to accelerate gender parity. This year’s theme: #ChooseToChallenge.

“From challenge comes change, so let’s all choose to challenge,” says the IWD website.

As IWD approached, Amazon Science asked women scientists from research areas across the company what three steps we can take as a society to forge a more gender-equal science community. Below are their responses.

Bouchra Bouqata

Bouchra is a senior applied scientist within Amazon Robotics. She earned her PhD in machine learning and artificial intelligence from Rensselaer Polytechnic Institute.

  • Provide a clear pipeline for advancing and promoting women’s careers in science. Companies and institutions should adopt gender-balanced peer review promotion processes and committees.  They should also provide special funds and grants to help women scientists further their research and work.
  • Conferences and publishing venues should adopt gender-conscious peer-review committee, and speaker- selection committee recruitment processes.
  • Companies and institutions should commit to educating everyone, not just leadership, to combat the issues facing women in science. They should provide gender awareness training as a standard component of any training they provide to their employees and members. They should provide seminars and convene roundtable discussions on gender issues in science to facilitate communication and identification of solutions.

Nilay Noyan Bulbul

Nilay is a principal scientist within the company’s Supply Chain Optimization Technologies organization.  She earned her PhD in operations research from Rutgers University.

  • Call the gender disparity out: Identify where women scientists are marginalized, and call out the disparity to ensure fair representation at the leadership of scientific research and decision-making, as well as “invite-only” prestigious roles, such as keynote speaking engagements, prize juries, and journal editorial board memberships.
  • Invest in the future: Create more initiatives and opportunities for the next generation of women scientists via mentoring and targeted prize and research fund calls.
  • Keep everyone accountable: Make sure every entity working towards gender equality in science community has a tangible way to measure the “change” and keep track of the progress, and make the process transparent.”

Cindy Cui

Cindy is a senior economist within the Alexa Shopping organization. She earned her PhD in economics from the University of Texas at Austin.  

Role models, aspirations, and supportive community are most important factors to me. Growing up, my grandma taught me reading and math. I still remember the days when we would go through math problems and I felt happy and proud when I solved them correctly.

My grandma is also one of the few female teachers in her generation and always emphasizes the importance of education and hard work. In school, many smart female classmates encouraged and challenged me throughout.

It takes all of us to improve gender equality in science, doing our best and helping others along the way.

Donna Dodson

Donna is a senior principal technologist within the AWS Security organization. She earned her master’s degree in computer science from Hood College.

  • Build a culture that values deep thinkers who balance speaking and listening to others. Often the subculture’s voices — including women’s — are not heard.
  • Create compensation, incentives, benefits, resources, recognition and a flexible workplace that balance needs at different stages in life. Early- and mid-career scientists with families require flexibility for a work-life balance.
  • Recognize and promote diverse voices throughout K-12 science programs to empower girls to grow their confidence in science knowledge, skills and abilities

Maryam Fazel-Zarandi

Maryam is a senior machine learning scientist within the Alexa AI organization. She earned her PhD in computer science at the University of Toronto.

Maryam Fazel-zarandi
Maryam Fazel-Zarandi
Credit: Pierce Harman Photography

I have been able to pursue my dream of becoming a scientist and have had access to role models and mentors throughout my education and career. The number of women scientists like me has increased over the past decades, however, we are still far from a gender equal science community.

While we should continue to reduce the large gap that still exists in terms of numbers, in my opinion, we should put more focus on mechanisms to retain women scientists. Lack of support for women in difficulty, feelings of isolation at work, and unmet expectations are among the top reasons why women leave their careers in science. The COVID-19 pandemic has further contributed to these difficulties as more women are taking additional caregiver roles at home, which in turn impacts their continued employment and career advancement.

To forge a more gender-equal global science community, we need to promote women’s integration in the research environment and workplace by learning about women’s experiences and providing direct support for women in difficulty. Our institutions and organizations should also implement and monitor measures to ensure womens’ career development in a post-pandemic world.

Rashmi Gangadharaiah

Rashmi is a senior research scientist within the AWS organization. She earned her PhD in information technology, artificial intelligence, and machine learning from Carnegie Mellon University.

As a woman and a mother of two girls, I’m glad that gender equality has been receiving more attention. Just talking about gender equality doesn’t mean that we’ve created a gender-equal community. Here are three steps that we can take to create a gender-equal global science community.

  1. Create opportunities that encourage more women to tackle challenging projects.
  2. Recognize women who have an impact on projects and give credit where it’s due.
  3. We as women should not be afraid to take on challenging projects, grab opportunities that come our way and have a community/support system when the deck is stacked against us.

Antia Lamas-Linares

Antia is a principal research scientist within the AWS Center for Quantum Computing. She earned her PhD in physics from the University of Oxford.

Helping diversify science is often not about actions within science, but immediately around science; removing the “death by a thousand cuts” problems.

The most impactful action we can take to improve science careers for women is to prioritize affordable childcare in research campuses (both university and industrial). This also has the very nice feature of benefiting the whole community of researchers, but it would have a disproportionate effect on women, while avoiding the insidious problems of preferential treatment.

If we can make space in campuses for exercise and culture, we can make space for daycares. A second thing we could do is prominently feature female scientists without remarking on their gender, they should not be an anomaly that needs to be highlighted and this narrative can be gently pushed from within organizations. Thirdly, and this is more of a personal action, actively avoid discouraging girls for pursuing geeky interests. Boys get rewarded with questions and attention for this behavior. Girls get the opposite signals.

Bilan Liu

Bilan is an applied robotics scientist within the company’s Lab126 organization. She earned her PhD in electrical and computer engineering at the University of Rochester.

  • The key aspect for a gender equal world is an environment where women share the same opportunities as men, such as quality education.
  • A gender equal world not only calls for the equality of women, but also quality among women. It is beneficial to share the recognition of successful women, as well as to have supportive peers and mentors for young women.
  • We should advocate to elevate women’s voices, both in the workplace and the media. Increasing the representation of women in a workplace not only creates a better workplace, it also changes perceptions about the value that women bring to the table.

Catherine Benoit Norris

Catherine is a science researcher within the company’s Sustainability organization.  She earned her PhD in business administration from the Université du Québec à Montréal.

  • Acknowledge and support workers, students, professionals, and scientists as parents. Until we fully recognize the needs of families, and have a work culture that allows setting limits, women will continue to be held back.
  • Make sure that everyone speaks and are listened to in meetings. Making sure that everybody is being heard and are being paid attention to when they speak is fundamental for a gender-equal global science community.
  • Support, encourage, value, and recognize women academic achievements. Publicly valuing, rewarding, and celebrating competence and achievements in women is a stepping stone towards gender equality in science and beyond.

Tara Taghavi

Tara is a senior applied scientist within the Alexa AI organization. She earned her PhD in computer science from the University of California, Los Angeles.

A first step in promoting gender equality is to involve more women in hiring processes, particularly hiring loops for science roles.

A second step is to facilitate a more favorable work environment for mothers by providing alternate hours, a reduced time schedule, and similar measures so women can grow their careers as they grow their families.

A third step is to empower women to take management roles. Many statistics have been shared regarding the disproportionate number of women who are promoted in comparison to their male counterparts. We should address it by encouraging women to pursue these roles, and then supporting them as they take on the responsibilities of these higher-level roles.

Nedelina Teneva

Nedelina is an applied scientist (search) within the Alexa organization. She earned her PhD in computer science from the University of Chicago.

Engaging in cross-disciplinary collaborations forces us to be curious, empowers us to say “I don’t know” and ask others “What do you think?”. 

This helps us better understand others’ lived experiences. In both professional and personal collaborations, we need to apply more rigorously the scientific method, which minimizes the influence of prejudice, by recognizing our biases or pre-existing beliefs and designing appropriate management strategies.

Finally, we need to continue to solidify these processes into platforms and organizations that nurture diverse opinions. Lessons learned from the existing diversity/inclusion efforts within the science community should be utilized in the broader society. 

Nikhita Vedula

Nikhita is an applied scientist with the Alexa Shopping organization. She earned her PhD in computer science and engineering from Ohio State University.

Education, encouragement, and awareness are key to fostering the growth of a more gender-equal science community.  Throughout my studies — straight through the completion of my PhD — I have seen at best an 80-20 ratio of men to women in classrooms, and academic or industrial positions. This needs to change, and this change needs to begin within our homes.    

Women require support from both men and other women alike, right from their childhood. We need to inspire and motivate women to nurture their dreams, and pursue their unique passions, instead of telling them things like “This field is for men, not for you”.

Research areas

Related content

US, WA, Redmond
Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Communications Engineer in Modeling and Simulation, this role is primarily responsible for the developing and analyzing high level system resource allocation techniques for links to ensure optimal system and network performance from the capacity, coverage, power consumption, and availability point of view. Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define novel wireless technology with few legacy constraints. The team develops and designs the communication system of Leo and analyzes its overall system level performance, such as overall throughput, latency, system availability, packet loss, etc., as well as compatibility for both connectivity and interference mitigation with other space and terrestrial systems. This role in particular will be responsible for 1) evaluating complex multi-disciplinary trades involving RF bandwidth and network resource allocation to customers, 2) understanding and designing around hardware/software capabilities and constraints to support a dynamic network topology, 3) developing heuristic or solver-based algorithms to continuously improve and efficiently use available resources, 4) demonstrating their viability through detailed modeling and simulation, 5) working with operational teams to ensure they are implemented. This role will be part of a team developing the necessary simulation tools, with particular emphasis on coverage, capacity, latency and availability, considering the yearly growth of the satellite constellation and terrestrial network. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum. Key job responsibilities • Work within a project team and take the responsibility for the Leo's overall communication system design and architecture • Extend existing code/tools and create simulation models representative of the target system, primarily in MATLAB • Design interconnection strategies between fronthaul and backhaul nodes. Analyze link availability, investigate link outages, and optimize algorithms to study and maximize network performance • Use RF and optical link budgets with orbital constellation dynamics to model time-varying system capacity • Conduct trade-off analysis to benefit customer experience and optimization of resources (costs, power, spectrum), including optimization of satellite constellation design and link selection • Work closely with implementation teams to simulate expected system level performance and provide quick feedback on potential improvements • Analyze and minimize potential self-interference or interference with other communication systems • Provide visualizations, document results, and communicate them across multi-disciplinary project teams to make key architectural decisions
US, WA, Seattle
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to apply their causal inference / structural econometrics skillsets to solve real world problems. The intern will work in the area of Store Economics and Science (SEAS) and develop models to SEAS. Our PhD Economist Internship Program offers hands-on experience in applied economics, supported by mentorship, structured feedback, and professional development. Interns work on real business and research problems, building skills that prepare them for full-time economist roles at Amazon and beyond. You will learn how to build data sets and perform applied econometric analysis 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. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. About the team The Stores Economics and Science Team (SEAS) is a Stores-wide interdisciplinary team at Amazon with a "peak jumping" mission focused on disruptive innovation. The team applies science, economics, and engineering expertise to tackle the business's most critical problems, working to move from local to global optima across Amazon Stores operations. SEAS builds partnerships with organizations throughout Amazon Stores to pursue this mission, exploring frontier science while learning from the experience and perspective of others. Their approach involves testing solutions first at a small scale, then aligning more broadly to build scalable solutions that can be implemented across the organization. The team works backwards from customers using their unique scientific expertise to add value, takes on long-run and high-risk projects that business teams typically wouldn't pursue, helps teams with kickstart problems by building practical prototypes, raises the scientific bar at Amazon, and builds and shares software that makes Amazon more productive.
US, TX, Austin
Amazon Security is seeking an Applied Scientist to work on GenAI acceleration within the Secure Third Party Tools (S3T) organization. The S3T team has bold ambitions to re-imagine security products that serve Amazon's pace of innovation at our global scale. This role will focus on leveraging large language models and agentic AI to transform third-party security risk management, automate complex vendor assessments, streamline controllership processes, and dramatically reduce assessment cycle times. You will drive builder efficiency and deliver bar-raising security engagements across Amazon. Key job responsibilities Own and drive end-to-end technical delivery for scoped science initiatives focused on third-party security risk management, independently defining research agendas, success metrics, and multi-quarter roadmaps with minimal oversight. Understanding approaches to automate third-party security review processes using state-of-the-art large language models, development intelligent systems for vendor assessment document analysis, security questionnaire automation, risk signal extraction, and compliance decision support. Build advanced GenAI and agentic frameworks including multi-agent orchestration, RAG pipelines, and autonomous workflows purpose-built for third-party risk evaluation, security documentation processing, and scalable vendor assessment at enterprise scale. Build ML-powered risk intelligence capabilities that enhance third-party threat detection, vulnerability classification, and continuous monitoring throughout the vendor lifecycle. Coordinate with Software Engineering and Data Engineering to deploy production-grade ML solutions that integrate seamlessly with existing third-party risk management workflows and scale across the organization. About the team Security is central to maintaining customer trust and delivering delightful customer experiences. At Amazon, our Security organization is designed to drive bar-raising security engagements. Our vision is that Builders raise the Amazon security bar when they use our recommended tools and processes, with no overhead to their business. Diverse Experiences Amazon Security values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why Amazon Security? At Amazon, security is central to maintaining customer trust and delivering delightful customer experiences. Our organization is responsible for creating and maintaining a high bar for security across all of Amazon’s products and services. We offer talented security professionals the chance to accelerate their careers with opportunities to build experience in a wide variety of areas including cloud, devices, retail, entertainment, healthcare, operations, and physical stores. Inclusive Team Culture In Amazon Security, it’s in our nature to learn and be curious. Ongoing DEI events and learning experiences inspire us to continue learning and to embrace our uniqueness. Addressing the toughest security challenges requires that we seek out and celebrate a diversity of ideas, perspectives, and voices. Training & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, training, and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why flexible work hours and arrangements are part of our culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve.
US, CA, Mountain View
At AWS Healthcare AI, we're revolutionizing healthcare delivery through AI solutions that serve millions globally. As a pioneer in healthcare technology, we're building next-generation services that combine Amazon's world-class AI infrastructure with deep healthcare expertise. Our mission is to accelerate our healthcare businesses by delivering intuitive and differentiated technology solutions that solve enduring business challenges. The AWS Healthcare AI organization includes services such as HealthScribe, Comprehend Medical, HealthLake, and more. We're seeking a Senior Applied Scientist to join our team working on our AI driven clinical solutions that are transforming how clinicians interact with patients and document care. Key job responsibilities To be successful in this mission, we are seeking an Applied Scientist to contribute to the research and development of new, highly influencial AI applications that re-imagine experiences for end-customers (e.g., consumers, patients), frontline workers (e.g., customer service agents, clinicians), and back-office staff (e.g., claims processing, medical coding). As a leading subject matter expert in NLU, deep learning, knowledge representation, foundation models, and reinforcement learning, you will collaborate with a team of scientists to invent novel, generative AI-powered experiences. This role involves defining research directions, developing new ML techniques, conducting rigorous experiments, and ensuring research translates to impactful products. You will be a hands-on technical innovator who is passionate about building scalable scientific solutions. You will set the standard for excellence, invent scalable, scientifically sound solutions across teams, define evaluation methods, and lead complex reviews. This role wields significant influence across AWS, Amazon, and the global research community.
US, WA, Seattle
Come be a part of a rapidly expanding $35 billion-dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. Amazon Business Data Insights and Analytics team is looking for a Data Scientist to lead the research and thought leadership to drive our data and insights strategy for Amazon Business. This role is central in shaping the definition and execution of the long-term strategy for Amazon Business. You will be responsible for researching, experimenting and analyzing predictive and optimization models, designing and implementing advanced detection systems that analyze customer behavior at registration and throughout their journey. You will work on ambiguous and complex business and research science problems with large opportunities. You'll leverage diverse data signals including customer profiles, purchase patterns, and network associations to identify potential abuse and fraudulent activities. You are an analytical individual who is comfortable working with cross-functional teams and systems, working with state-of-the-art machine learning techniques and AWS services to build robust models that can effectively distinguish between legitimate business activities and suspicious behavior patterns You must be a self-starter and be able to learn on the go. Excellent written and verbal communication skills are required as you will work very closely with diverse teams. Key job responsibilities - Interact with business and software teams to understand their business requirements and operational processes - Frame business problems into scalable solutions - Adapt existing and invent new techniques for solutions - Gather data required for analysis and model building - Create and track accuracy and performance metrics - Prototype models by using high-level modeling languages such as R or in software languages such as Python. - Familiarity with transforming prototypes to production is preferred. - Create, enhance, and maintain technical documentation
US, TX, Austin
Amazon Leo is an initiative to launch a constellation of Low Earth Orbit satellites that will provide low-latency, high-speed broadband connectivity to unserved and underserved communities around the world. As a Systems Engineer, this role is primarily responsible for the design, development and integration of communication payload and customer terminal systems. The Role: Be part of the team defining the overall communication system and architecture of Amazon Leo’s broadband wireless network. This is a unique opportunity to innovate and define groundbreaking wireless technology at global scale. The team develops and designs the communication system for Leo and analyzes its overall system level performance such as for overall throughput, latency, system availability, packet loss etc. This role in particular will be responsible for leading the effort in designing and developing advanced technology and solutions for communication system. This role will also be responsible developing advanced physical layer + protocol stacks systems as proof of concept and reference implementation to improve the performance and reliability of the LEO network. In particular this role will be responsible for using concepts from digital signal processing, information theory, wireless communications to develop novel solutions for achieving ultra-high performance LEO network. This role will also be part of a team and develop simulation tools with particular emphasis on modeling the physical layer aspects such as advanced receiver modeling and abstraction, interference cancellation techniques, FEC abstraction models etc. This role will also play a critical role in the integration and verification of various HW and SW sub-systems as a part of system integration and link bring-up and verification. Export Control Requirement: Due to applicable export control laws and regulations, candidates must be a U.S. citizen or national, U.S. permanent resident (i.e., current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.
US, MA, N.reading
Amazon Industrial Robotics Group is seeking exceptional talent to help develop the next generation of advanced robotics systems that will transform automation at Amazon's scale. We're building revolutionary robotic systems that combine cutting-edge AI, sophisticated control systems, and advanced mechanical design to create adaptable automation solutions capable of working safely alongside humans in dynamic environments. This is a unique opportunity to shape the future of robotics and automation at an unprecedented scale, working with world-class teams pushing the boundaries of what's possible in robotic dexterous manipulation, locomotion, and human-robot interaction. This role presents an opportunity to shape the future of robotics through innovative applications of deep learning and large language models. At Amazon Industrial Robotics Group, we leverage advanced robotics, machine learning, and artificial intelligence to solve complex operational challenges at an unprecedented scale. Our fleet of robots operates across hundreds of facilities worldwide, working in sophisticated coordination to fulfill our mission of customer excellence. We are pioneering the development of dexterous manipulation system that: - Enables unprecedented generalization across diverse tasks - Enables contact-rich manipulation in different environments - Seamlessly integrates low-level skills and high-level behaviors - Leverage mechanical intelligence, multi-modal sensor feedback and advanced control techniques. The ideal candidate will contribute to research that bridges the gap between theoretical advancement and practical implementation in robotics. You will be part of a team that's revolutionizing how robots learn, adapt, and interact with their environment. Join us in building the next generation of intelligent robotics systems that will transform the future of automation and human-robot collaboration. A day in the life - Work on design and implementation of methods for Visual SLAM, navigation and spatial reasoning - Leverage simulation and real-world data collection to create large datasets for model development - Develop a hierarchical system that combines low-level control with high-level planning - Collaborate effectively with multi-disciplinary teams to co-design hardware and algorithms for dexterous manipulation
US, NY, New York
We are seeking an Applied Scientist to lead the development of evaluation frameworks and data collection protocols for robotic capabilities. In this role, you will focus on designing how we measure, stress-test, and improve robot behavior across a wide range of real-world tasks. Your work will play a critical role in shaping how policies are validated and how high-quality datasets are generated to accelerate system performance. You will operate at the intersection of robotics, machine learning, and human-in-the-loop systems, building the infrastructure and methodologies that connect teleoperation, evaluation, and learning. This includes developing evaluation policies, defining task structures, and contributing to operator-facing interfaces that enable scalable and reliable data collection. The ideal candidate is highly experimental, systems-oriented, and comfortable working across software, robotics, and data pipelines, with a strong focus on turning ambiguous capability goals into measurable and actionable evaluation systems. Key job responsibilities - Design and implement evaluation frameworks to measure robot capabilities across structured tasks, edge cases, and real-world scenarios - Develop task definitions, success criteria, and benchmarking methodologies that enable consistent and reproducible evaluation of policies - Create and refine data collection protocols that generate high-quality, task-relevant datasets aligned with model development needs - Build and iterate on teleoperation workflows and operator interfaces to support efficient, reliable, and scalable data collection - Analyze evaluation results and collected data to identify performance gaps, failure modes, and opportunities for targeted data collection - Collaborate with engineering teams to integrate evaluation tooling, logging systems, and data pipelines into the broader robotics stack - Stay current with advances in robotics, evaluation methodologies, and human-in-the-loop learning to continuously improve internal approaches - Lead technical projects from conception through production deployment - Mentor junior scientists and engineers
US, NY, New York
The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading 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 Global Optimization (GO) team within Sponsored Products and Brands at Amazon Ads is re-imagining the advertising stack from the ground up across 20+ marketplaces. We are seeking an experienced Senior Data Scientist to join our team. You will develop scalable analytical approaches to evaluate marketplace performance across the entire Ads stack to uncover regional and marketplace-specific insights, design and run experiments, and shape our development roadmap. We operate as a closely integrated team of Data Scientists, Applied Scientists, and Engineers to translate data-driven insights into measurable business impact. If you're energized by solving complex challenges at international scale and pushing the boundaries of what's possible with GenAI, join us in shaping the future of global advertising at Amazon. Key job responsibilities As a Data Scientist on this team, you will: - Write code to obtain, manipulate, and analyze data to derive business insights. - Apply statistical and ML knowledge to specific business problems and data. - Analyze historical data to identify trends and support optimal decision making. - Formalize assumptions about how our systems are expected to work and develop methods to systematically identify high ROI improvements. About the team SPB Global Optimization (GO) team was created to accelerate growth in non-US markets. We are driving business growth across all marketplaces by creating delightful experiences for shoppers and advertisers alike. We are working backwards from customers to re-imagine Amazon's advertising stack from the ground up, leveraging GenAI to deliver solutions that scale across 20+ marketplaces from day one.
US, WA, Seattle
Unleash Your Potential as an AI Trailblazer At Amazon, we're on a mission to revolutionize the way people discover and access information. Our Applied Science team is at the forefront of this endeavor, pushing the boundaries of recommender systems and information retrieval. We're seeking brilliant minds to join us as interns and contribute to the development of cutting-edge AI solutions that will shape the future of personalized experiences. As an Applied Science Intern focused on Recommender Systems and Information Retrieval in Machine Learning, you'll have the opportunity to work alongside renowned scientists and engineers, tackling complex challenges in areas such as deep learning, natural language processing, and large-scale distributed systems. Your contributions will directly impact the products and services used by millions of Amazon customers worldwide. Imagine a role where you immerse yourself in groundbreaking research, exploring novel machine learning models for product recommendations, personalized search, and information retrieval tasks. You'll leverage natural language processing and information retrieval techniques to unlock insights from vast repositories of unstructured data, fueling the next generation of AI applications. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Join us at the forefront of applied science, where your contributions will shape the future of AI and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology. Must be eligible and available for a full-time (40h / week) 12 week internship between May 2026 and September 2026 Amazon has positions available for Machine Learning Applied Science Internships in, but not limited to Arlington, VA; Bellevue, WA; Boston, MA; New York, NY; Palo Alto, CA; San Diego, CA; Santa Clara, CA; Seattle, WA. Key job responsibilities We are particularly interested in candidates with expertise in: Knowledge Graphs and Extraction, Programming/Scripting Languages, Time Series, Machine Learning, Natural Language Processing, Deep Learning,Neural Networks/GNNs, Large Language Models, Data Structures and Algorithms, Graph Modeling, Collaborative Filtering, Learning to Rank, Recommender Systems In this role, you'll collaborate with brilliant minds to develop innovative frameworks and tools that streamline the lifecycle of machine learning assets, from data to deployed models in areas at the intersection of Knowledge Management within Machine Learning. You will conduct groundbreaking research into emerging best practices and innovations in the field of ML operations, knowledge engineering, and information management, proposing novel approaches that could further enhance Amazon's machine learning capabilities. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. A day in the life - Design, implement, and experimentally evaluate new recommendation and search algorithms using large-scale datasets - Develop scalable data processing pipelines to ingest, clean, and featurize diverse data sources for model training - Conduct research into the latest advancements in recommender systems, information retrieval, and related machine learning domains - Collaborate with cross-functional teams to integrate your innovative solutions into production systems, impacting millions of Amazon customers worldwide - Communicate your findings through captivating presentations, technical documentation, and potential publications, sharing your knowledge with the global AI community