Sergey Menis is seen outside on a sunny day with a colorful landscape of mountains behind him, Sergey is standing to the left with his arms crossed, looking into the camera
Sergey Menis developed the nanoparticle platform that underpins a promising HIV vaccine candidate. The nanoparticle Menis designed snaps together with a protein, eOD-GT8, which is optimized to stimulate production of the rare antibodies that can eventually become bnAbs.
Courtesy of Sergey Menis

Amazon scientist Sergey Menis contributes to development of vaccine approach against HIV

"I hope we have accelerated HIV vaccine development by providing findings that we and others can build on."

About 16 years ago, Sergey Menis was volunteering at a protein design lab during the day and parking cars at night. He'd come to the Baker Lab in Seattle on a whim. While earning his master's degree in bioinformatics at Chalmers University of Technology in Sweden, he read a 2003 paper describing the lab's work designing a novel protein that didn't exist in nature.

"I was just in awe of that power," Menis recalled. He wanted to learn more about biochemist David Baker's work and emailed asking to join the lab, which is based at the University of Washington. Once there, he opted to work with Bill Schief, a postdoctoral researcher with Baker who was just starting his own lab. But Schief noticed Menis wasn't fully present in his work — he often seemed sleepy. What was going on?

Related content
Using social media data, the University of Maryland's Philip Resnik aims to help clinicians prioritize individuals who may need immediate attention.

Menis explained about the night job at the car park. He wanted to do more at the lab, but after all, he had to pay rent. Schief asked how much Menis needed to cover his expenses. Then he hired him.

That job was a turning point.

Schief and his team, along with Menis, developed a breakthrough approach to a vaccine for HIV. In February 2021, the nonprofit scientific research organizations IAVI and Scripps Research announced exciting results in a phase 1 clinical trial — called IAVI G001 — of the Schief lab's vaccine candidate. A phase I trial represents the first time a vaccine is tested in humans, one step in what is typically a four-phase process that determines its safety, efficacy, and proper dosage. In this case, the promising vaccine produced the desired immune response in 97% of participants.

HIV vaccine approach succeeds in first clinical trial

Earlier this year, building on those results, IAVI and Moderna announced that first doses had been administered in a new clinical trial of the experimental HIV vaccine. IAVI officials noted this portion of the phase 1 trial, called IAVI G002, will test the ability to prime and further mature the desired immune response using Moderna’s messenger RNA (mRNA) delivery platform used for their coronavirus vaccines. The mRNA platform enables rapid vaccine production that may dramatically accelerate the development timeline.

Guided by curiosity

Menis, who joined Amazon as a scientist in November 2020 and is now a solution architect with Amazon Web Services (AWS), hadn't set out to be a biomedical researcher, or even a scientist. "I never had a career in mind, in general," he said. "I would just follow whatever looked interesting."

As an undergrad at the University of Florida, that meant computer science. It wasn't until he had obtained his master's degree in software engineering and begun working at the defense and aerospace company Lockheed Martin that he started to rethink his career path.

Related content
Dr. Kristina Simonyan and her team created an AI-based deep learning platform that offers patients some peace of mind.

Writing software for government contract projects was fine, but it didn't feel hands-on enough. "I wanted to see more feedback and results, faster," Menis said.

He recalled a bioinformatics elective class that he'd taken while in grad school at the University of Central Florida. On another fateful impulse, he decided to look at bioinformatics grad programs; this time in Europe, as he was in search of a change of scenery. He got accepted to Chalmers University of Technology and, without knowing much about the university, headed to Sweden.

"Even though it's a well-known school in certain circles, I wasn't even sure it was a real school until I arrived there," he said, laughing. "But it turned out to be a fantastic school and a really intense program."

And when he read about David Baker's work in inventing a protein molecule from scratch, the next chapter of his career — computational protein design — began to unfold.

HIV: a formidable foe

Human immunodeficiency virus has infected more than 75 million people and killed more than 32 million since the epidemic began in early 1980s. With the isolation of the virus in the mid-1980s, it seemed that a vaccine was in the offing. But conventional approaches, which involve taking some inactivated part of the virus to stimulate an immune response, have not worked for HIV.

The virus has multiple wily strategies it employs to hide within the body. It cloaks itself with sugars that make it nearly invisible to the human immune system. And its surface is always changing, a series of disguises that fool most enemy antibodies. But researchers have identified a ray of hope buried within the immune system: the potential to make bnAbs, which can recognize and defeat 99% of HIV strains.

Sergey Menis is seen in a lab setting, wearing gloves while holding a device
Sergey Menis said when he read about David Baker's work in inventing a protein molecule from scratch, the next chapter of his career — computational protein design — began to unfold.
Courtesy of Sergey Menis

The problem is, people don't develop bnAbs until they're years into an infection. “That's too little, too late," Menis said. "By the time you've actually started developing the responses you need, you're already productively infected."

The strategy researchers are pursuing is to initiate the process of making these potent antibodies before infection occurs, giving the body a head start. To do so, they must identify the right "baby antibodies," as Menis calls them, and train them to be bnAbs.

Given that the human body has the ability to make an estimated 1 quintillion unique antibodies, finding and training the right ones is a needle-in-the-haystack endeavor. And only certain antibodies have the ability to become bnAbs—those baby antibodies are literally one in a million.

Only a small fraction of people with HIV develop the most potent bnAb response — the kind an effective vaccine would elicit — on their own. Researchers have been able to zero in on these antibodies by analyzing blood from HIV-positive donors. But there's good news, and the recent clinical trial confirmed it.

Related content
Politecnico di Milano professor Stefano Ceri is working to integrate genomic datasets into a single accessible system with the support of an Amazon Machine Learning Research Award.

"Nearly everyone in the world should have the cells needed to start the process of producing this immune response," Menis said. "To get that process started, we need to find them, stimulate them, and have them multiply."

Building a vaccine platform

After Menis began working at Baker Lab, he decided to pursue a PhD in biochemistry at the University of Washington in the Schief lab. Menis moved to San Diego midway through his PhD studies when Schief moved his lab to Scripps Research and IAVI.

"Sergey is very thoughtful and calm, with meticulous attention to detail. He is curious about how things work," said Schief, who is executive director of vaccine design for IAVI’s Neutralizing Antibody Center (NAC) at Scripps Research and a professor in the Department of Immunology and Microbiology at Scripps.

Schief advised Menis on his PhD thesis, during which Menis developed the nanoparticle platform that underpins the HIV vaccine candidate. The nanoparticle Menis designed snaps together with a protein, eOD-GT8, which is optimized to stimulate production of the rare antibodies that can eventually become bnAbs. The eOD-GT8 protein was developed primarily by another PhD student in the Schief lab, Joe Jardine. The nanoparticle amplifies the body's response by delivering multiple copies of eOD-GT8.

A computer image of the eOD-GT8 immune-stimulating protein.
A computer image of the eOD-GT8 immune-stimulating protein.
Courtesy of Sergey Menis

"It's spherical, like a virus, so the immune system treats it as if it might be a virus of some kind," Menis said. "We want to make it look like a little virus, even though it has no infectious properties whatsoever."

Menis served as the Schief lab's subject matter expert during the multi-year process of developing the vaccine candidate. "He played a big role in planning and carrying out the clinical trial," Schief said.

A team effort

Both Menis and Schief are careful to emphasize that there is much more to do before an approved HIV vaccine becomes reality. While the results from IAVI G001 are encouraging, there are significant milestones remaining.

"By demonstrating that this concept works in humans, and actually can work very well in terms of eliciting strong and consistent responses of the kind we wanted, I hope we have accelerated HIV vaccine development by providing findings that we and others can build on," Schief said.

Related content
Amazon Research Award recipient Jonathan Tamir is focusing on deriving better images faster.

Menis is also quick to credit the 48 volunteers who participated in the IAVI G001 clinical trial, noting that without such volunteers, a vaccine wouldn’t be possible. "They are the co-creators of this effort," he said. Schief and Menis also praised the work of many other individuals, particularly colleagues at Fred Hutch, George Washington University, and the NIH Vaccine Research Center.

The upcoming IAVI G002 will recruit 56 volunteers across four sites: GWU School of Medicine and Health, Hope Clinic of Emory Vaccine Center in Atlanta, Fred Hutchinson Cancer Research Center in Seattle, and the University of Texas–Health Science Center at San Antonio. The goal: replicate the priming of “baby antibodies” observed in IAVI G001 and teach them to take a step towards becoming a bnAb capable of neutralizing HIV.

An intriguing offer

Menis was working at IAVI and preparing to go on vacation when Amazon contacted him in 2020, asking whether he'd be interested in a position at the company. The hiring process happened quickly: He did an interview while on the trip, and on his first day back from vacation, he had an offer in his inbox.

"When Amazon reached out, I was really intrigued by the possibilities of what a giant like Amazon could be doing," Menis said. "I was open to discovering what that meant."

Related content
Gari Clifford, the chair of the Department of Biomedical Informatics at Emory University and an Amazon Research Award recipient, wants to transform healthcare.

After spending a little over a year as a research scientist, Menis moved into a senior product manager role with Amazon Diagnostics and then transitioned into a role as a solution architect with AWS, building solutions for healthcare and life sciences startups. “For me, the roles represent opportunities to learn and be curious,” Menis said, citing one of Amazon’s leadership principles.

He admitted he didn’t know much about the principles until his first job interview, but now he has come to appreciate them. He enjoys seeing how they relate to him and his past work.

“Working at Amazon has been a learning experience,” he says — yet another on the journey from lab volunteer to medical-breakthrough-creating scientist to whatever the next chapter will be.

Related content

US, NY, New York
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist to work on pre-training methodologies for Generative Artificial Intelligence (GenAI) models. You will interact closely with our customers and with the academic and research communities. Key job responsibilities Join us to work as an integral part of a team that has experience with GenAI models in this space. We work on these areas: - Scaling laws - Hardware-informed efficient model architecture, low-precision training - Optimization methods, learning objectives, curriculum design - Deep learning theories on efficient hyperparameter search and self-supervised learning - Learning objectives and reinforcement learning methods - Distributed training methods and solutions - AI-assisted research About the team The AGI team has a mission to push the envelope in GenAI with Large Language Models (LLMs) and multimodal systems, in order to provide the best-possible experience for our customers.
US, WA, Seattle
Are you a passionate Applied Scientist (AS) ready to shape the future of digital content creation? At Amazon, we're building Earth's most desired destination for creators to monetize their unique skills, inspire the next generation of customers, and help brands expand their reach. We build innovative products and experiences that drive growth for creators across Amazon's ecosystem. Our team owns the entire Creator product suite, ensuring a cohesive experience, optimizing compensation structures, and launching features that help creators achieve both monetary and non-monetary goals. Key job responsibilities As an AS on our team, you will: Handle challenging problems that directly impact millions of creators and customers Independently collect and analyze data Develop and deliver scalable predictive models, using any necessary programming, machine learning, and statistical analysis software Collaborate with other scientists, engineers, product managers, and business teams to creatively solve problems, measure and estimate risks, and constructively critique peer research Consult with engineering teams to design data and modeling pipelines which successfully interface with new and existing software Participate in design and implementation across teams to contribute to initiatives and develop optimal solutions that benefit the creators organization Key job responsibilities he successful candidate is a self-starter, comfortable with a dynamic, fast-paced environment, and able to think big while paying careful attention to detail. You have deep knowledge of an area/multiple areas of science, with a track record of applying this knowledge to deliver science solutions in a business setting and a demonstrated ability to operate at scale. You excel in a culture of invention and collaboration.
US, MA, North Reading
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 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. Amazon Robotics is seeking Applied Science Interns and Co-ops with a passion for robotic research to work on algorithms for robotics. Our team works on challenging and high-impact projects within robotics. Examples of projects include allocating resources to complete million orders a day, coordinating the motion of thousands of robots, autonomous navigation in warehouses, identifying objects and damage, and learning how to grasp all the products Amazon sells. As an Applied Science Intern/Co-op at Amazon Robotics, you will be working on one or more of our robotic technologies such as autonomous mobile robots, robot manipulators, and AI, computer vision technologies. The intern/co-op project(s) and the internship/co-op location are based on the team the student will be working on. Please note that by applying to this role you would be considered for Applied Scientist summer intern, spring co-op, and fall co-op roles on various Amazon Robotics teams. These teams work on robotics research within areas such as computer vision, machine learning, robotic manipulation, mobile robotics, navigation, path planning, perception, optimization and more. Learn more about Amazon Robotics: https://amazon.jobs/en/teams/amazon-robotics https://www.aboutamazon.com/news/operations/amazon-robotics-robots-fulfillment-center https://www.aboutamazon.com/news/operations/amazon-million-robots-ai-foundation-model
US, NY, New York
Are you passionate about conducting research to develop and grow leaders? Would you like to impact more than 1M Amazonians globally and improve the employee experience? If so, you should consider joining the People eXperience & Technology Central Science (PXTCS) team. Our goal is to be best and most diverse workforce in the world. PXTCS uses science, research, and technology to optimize employee experience and performance across the full employee lifecycle, from first contact through exit. We use economics, behavioral science, statistics, and machine learning to proactively identify mechanisms and process improvements which simultaneously improve Amazon and the lives, wellbeing, and the value of work to Amazonians. We are an interdisciplinary team that combines the talents of science and engineering to develop and deliver solutions that measurably achieve this goal. This individual should be skilled in core data science tools and methods, icnluding SQL, a statistical software package (e.g., R, Python, or Stata), inferential statistics, and proficient in machine learning. This person should also have strong business acumen to navigate complex, ambiguous business challenges — they should be adept at asking the right questions, knowing what methodologies to use (and why), efficiently analyzing massive datasets, and communicating results to multiple audiences (e.g., technical peers, functional teams, business leaders). In order to move quickly, deliver high-quality results, and adapt to ever-evolving business priorities, effective communication skills in research fundamentals (e.g., research design, measurement, statistics) will also be a must. Major responsibilities will include: - Managing the full life cycle of large-scale research initiatives across multiple business segments that impact leaders in our organization (i.e., develop strategy, gather requirements, manage, and execute) - Serving as a subject matter expert on a wide variety of topics related to research design, measurement, analysis - Working with internal partners and external stakeholders to evaluate research initiatives that provide bottom-line ROI and incremental improvements over time - Collaborating with a cross-functional team that has expertise in social science, machine learning, econometrics, psychometrics, natural language processing, forecasting, optimization, business intelligence, analytics, and policy evaluation - Ability to query and clean complex datasets from multiple sources, to funnel into advanced statistical analysis - Writing high-quality, evidence-based documents that help provide insights to business leaders and gain buy-in - Sharing knowledge, advocating for innovative solutions, and mentoring others Inclusive Team Culture Here at Amazon, we embrace our differences. We are committed to furthering our culture of inclusion. We have 12 affinity groups (employee resource groups) with more than 1M employees across hundreds of chapters around the world. 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. Amazon’s culture of inclusion is reinforced within our 14 Leadership Principles, which reminds team members to seek diverse perspectives, learn and be curious, and earn trust. Flexibility It isn’t about which 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 offer flexibility and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth We care about your career growth, too. Whether your goals are to explore new technologies, take on bigger opportunities, or get to the next level, we'll help you get there. Our business is growing fast and our people will grow with it. About the team We are a collegial and multidisciplinary team of researchers in People eXperience and Technology (PXT) that combines the talents of science and engineering to develop innovative solutions to make Amazon Earth's Best Employer. We leverage data and rigorous analysis to help Amazon attract, retain, and develop one of the world’s largest and most talented workforces.
IN, KA, Bengaluru
Alexa is the voice activated digital assistant powering devices like Amazon Echo, Echo Dot, Echo Show, and Fire TV, which are at the forefront of this latest technology wave. To preserve our customers’ experience and trust, the Alexa Sensitive Content Intelligence (ASCI) team creates policies and builds services and tools through Machine Learning techniques to detect and mitigate sensitive content across Alexa. We are looking for an experienced Applied Science Manager to build industry-leading technologies in attribute extraction and sensitive content detection across all languages and countries. Key job responsibilities An Applied Scientist II will be working with a team of exceptional scientists to develop novel algorithms and modeling techniques to advance the state of the art in NLP related tasks. You will work in a hybrid, fast-paced organization where scientists, engineers, and product managers work together to build customer facing experiences. You will collaborate with and mentor other junior scientists to raise the bar of scientific research in Amazon. Your work will directly impact our customers in the form of products and services that make use of speech, language, and computer vision technologies. We are looking for a candidate with strong technical experiences a passion for building scientific driven solutions in a fast-paced environment. You should have good understanding of NLP models (e.g. LSTM, transformer based models) and where to apply them in different business cases. You leverage your exceptional technical expertise, a sound understanding of the fundamentals of Computer Science, and practical experience of building large-scale distributed systems to creating reliable, scalable, and high-performance products. In addition to technical depth, you must possess exceptional communication skills and understand how to influence key stakeholders. You will be joining a select group of people making history producing one of the most highly rated products in Amazon's history, so if you are looking for a challenging and innovative role where you can solve important problems while growing as a leader, this may be the place for you. A day in the life You will be working with a group of talented scientists on researching algorithm and running experiments to test scientific proposal/solutions to improve our sensitive contents detection and mitigation. This will involve collaboration with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. You will mentor other scientists, review and guide their work, help develop roadmaps for the team. You work closely with partner teams across Alexa to deliver platform features that require cross-team leadership. About the team The mission of the Alexa Sensitive Content Intelligence (ASCI) team is to (1) minimize negative surprises to customers caused by sensitive content, (2) detect and prevent potential brand-damaging interactions, and (3) build customer trust through appropriate interactions on sensitive topics. The term “sensitive content” includes within its scope a wide range of categories of content such as offensive content (e.g., hate speech, racist speech), profanity, content that is suitable only for certain age groups, politically polarizing content, and religiously polarizing content. The term “content” refers to any material that is exposed to customers by Alexa (including both 1P and 3P experiences) and includes text, speech, audio, and video. Job responsibilities
US, MA, Boston
As a Principal Scientist within the Artificial General Intelligence (AGI) organization, you are a trusted part of the technical leadership. You bring business and industry context to science and technology decisions. You set the standard for scientific excellence and make decisions that affect the way we build and integrate algorithms. You solicit differing views across the organization and are willing to change your mind as you learn more. Your artifacts are exemplary and often used as reference across organization. You are a hands-on scientific leader. Your solutions are exemplary in terms of algorithm design, clarity, model structure, efficiency, and extensibility. You tackle intrinsically hard problems, acquiring expertise as needed. You decompose complex problems into straightforward solutions. You amplify your impact by leading scientific reviews within your organization or at your location. You scrutinize and review experimental design, modeling, verification and other research procedures. You probe assumptions, illuminate pitfalls, and foster shared understanding. You align teams toward coherent strategies. You educate, keeping the scientific community up to date on advanced techniques, state of the art approaches, the latest technologies, and trends. You help managers guide the career growth of other scientists by mentoring and play a significant role in hiring and developing scientists and leads. You will play a critical role in driving the development of Generative AI (GenAI) technologies that can handle Amazon-scale use cases and have a significant impact on our customers' experiences. Key job responsibilities You will be responsible for defining key research directions, adopting or inventing new machine learning techniques, conducting rigorous experiments, publishing results, and ensuring that research is translated into practice. You will develop long-term strategies, persuade teams to adopt those strategies, propose goals and deliver on them. You will also participate in organizational planning, hiring, mentorship and leadership development. You will be technically fearless and with a passion for building scalable science and engineering solutions. You will serve as a key scientific resource in full-cycle development (conception, design, implementation, testing to documentation, delivery, and maintenance). About the team The AGI team has a mission to push the envelope with multimodal LLMs and Gen AI in Computer Vision, in order to provide the best-possible experience for our customers.
US, NY, New York
We are looking for a passionate Applied Scientist to help pioneer the next generation of agentic AI applications for Amazon advertisers. In this role, you will design agentic architectures, develop tools and datasets, and contribute to building systems that can reason, plan, and act autonomously across complex advertiser workflows. You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers—delivering customer-facing products that directly help them create, optimize, and grow their campaigns. Beyond building models, you will advance the agent ecosystem by experimenting with and applying core primitives such as tool orchestration, multi-step reasoning, and adaptive preference-driven behavior. This role requires working independently on ambiguous technical problems, collaborating closely with scientists, engineers, and product managers to bring innovative solutions into production. Key job responsibilities - Design and build agents to guide advertisers in conversational and non-conversational experience. - Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO). - Curate datasets and tools for MCP. - Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails. - Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning. - Prototype and iterate on multi-agent orchestration frameworks and workflows. - Collaborate with peers across engineering and product to bring scientific innovations into production. - Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest 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 Advertiser Guidance team within Sponsored Products and Brands is focused on guiding and supporting 1.6MM advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware agentic advertiser guidance system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as proactively delivering guidance based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.
CA, ON, Toronto
We are looking for a passionate Applied Scientist to help pioneer the next generation of agentic AI applications for Amazon advertisers. In this role, you will design agentic architectures, develop tools and datasets, and contribute to building systems that can reason, plan, and act autonomously across complex advertiser workflows. You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers—delivering customer-facing products that directly help them create, optimize, and grow their campaigns. Beyond building models, you will advance the agent ecosystem by experimenting with and applying core primitives such as tool orchestration, multi-step reasoning, and adaptive preference-driven behavior. This role requires working independently on ambiguous technical problems, collaborating closely with scientists, engineers, and product managers to bring innovative solutions into production. Key job responsibilities - Lead business, science and engineering strategy and roadmap for Sponsored Products Agentic Advertiser Guidance. - Design and build agents to guide advertisers in conversational and non-conversational experience. - Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO). - Curate datasets and tools for MCP. - Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails. - Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning. - Prototype and iterate on multi-agent orchestration frameworks and workflows. - Collaborate with peers across engineering and product to bring scientific innovations into production. - Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest 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 Advertiser Guidance team within Sponsored Products and Brands is focused on guiding and supporting 1.6MM advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware agentic advertiser guidance system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as proactively delivering guidance based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.
US, CA, Palo Alto
We are looking for a passionate Applied Scientist to help pioneer the next generation of agentic AI applications for Amazon advertisers. In this role, you will design agentic architectures, develop tools and datasets, and contribute to building systems that can reason, plan, and act autonomously across complex advertiser workflows. You will work at the forefront of applied AI, developing methods for fine-tuning, reinforcement learning, and preference optimization, while helping create evaluation frameworks that ensure safety, reliability, and trust at scale. You will work backwards from the needs of advertisers—delivering customer-facing products that directly help them create, optimize, and grow their campaigns. Beyond building models, you will advance the agent ecosystem by experimenting with and applying core primitives such as tool orchestration, multi-step reasoning, and adaptive preference-driven behavior. This role requires working independently on ambiguous technical problems, collaborating closely with scientists, engineers, and product managers to bring innovative solutions into production. Key job responsibilities - Lead business, science and engineering strategy and roadmap for Sponsored Products Agentic Advertiser Guidance. - Design and build agents to guide advertisers in conversational and non-conversational experience. - Design and implement advanced model and agent optimization techniques, including supervised fine-tuning, instruction tuning and preference optimization (e.g., DPO/IPO). - Curate datasets and tools for MCP. - Build evaluation pipelines for agent workflows, including automated benchmarks, multi-step reasoning tests, and safety guardrails. - Develop agentic architectures (e.g., CoT, ToT, ReAct) that integrate planning, tool use, and long-horizon reasoning. - Prototype and iterate on multi-agent orchestration frameworks and workflows. - Collaborate with peers across engineering and product to bring scientific innovations into production. - Stay current with the latest research in LLMs, RL, and agent-based AI, and translate findings into practical applications. About the team The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through the latest 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 Advertiser Guidance team within Sponsored Products and Brands is focused on guiding and supporting 1.6MM advertisers to meet their advertising needs of creating and managing ad campaigns. At this scale, the complexity of diverse advertiser goals, campaign types, and market dynamics creates both a massive technical challenge and a transformative opportunity: even small improvements in guidance systems can have outsized impact on advertiser success and Amazon’s retail ecosystem. Our vision is to build a highly personalized, context-aware agentic advertiser guidance system that leverages LLMs together with tools such as auction simulations, ML models, and optimization algorithms. This agentic framework, will operate across both chat and non-chat experiences in the ad console, scaling to natural language queries as well as proactively delivering guidance based on deep understanding of the advertiser. To execute this vision, we collaborate closely with stakeholders across Ad Console, Sales, and Marketing to identify opportunities—from high-level product guidance down to granular keyword recommendations—and deliver them through a tailored, personalized experience. Our work is grounded in state-of-the-art agent architectures, tool integration, reasoning frameworks, and model customization approaches (including tuning, MCP, and preference optimization), ensuring our systems are both scalable and adaptive.
US, CA, Palo Alto
About Sponsored Products and Brands The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through 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. Key job responsibilities As an Applied Science Manager, you will: * Directly manage and lead a cross-functional team of Applied Scientists, Machine Learning Engineers, and Software Development Engineers. * Develop science and engineering roadmaps for SPB ads response prediction with ML and Gen AI solutions, run annual planning, and foster cross-team collaboration on model development and integration to advertising applications. * Hire and develop top talent, provide technical and career development guidance to scientists and engineers within and across the organization. * Stay informed about recent scientific publications, industrial research trends, and system designs that are pertinent to the SPB advertising business and bring those insights with the team. About the team The Ad Response Prediction team within Sponsored Products and Brands (SPB) drives personalized shopping experiences for SPB Ads across placements, pages, and devices worldwide. We achieve this through ML and GenAI solutions that include customized shopper response prediction and session-level understanding to optimize every stage of the ad-serving process, from sourcing and bidding to widget discovery and auctions. Our responsibilities include advancing response prediction through model and feature innovations and extending prediction beyond the auction stage to areas such as targeting, sourcing, and bidding. We are seeking an Applied Science Manager with a strong background in ML and Gen AI solutions. The ideal candidate shall have experience managing both scientists and engineers and will be passionate about applying these technologies to the advertising domain.