Can you teach a computer to smell? Osmo is trying

The company’s work, supported by the Amazon Alexa Fund, has relevant applications for areas from perfumes to disease detection.

At the age of 12, Alex Wiltschko bought his first perfume, Azzaro pour Homme. He’d read about it in his favorite book — Perfumes: The Guide, by Luca Turin — and was thrilled to find it at a knock-down price at his local TJ Maxx. It would be the first in a large collection.

For as long as he can remember, Wiltschko has been obsessed by scent. “It’s how I’m wired,” he says. His other obsession? Computers. “An interest in perfumes and computers was not the recipe for social success as an adolescent,” he adds.

It was, however, the recipe for a life trajectory that took Wiltschko deep into the neuroscience of olfaction and cutting-edge machine learning. This combination has placed Wiltschko at the forefront of the nascent science of digital olfaction — a.k.a. giving computers a sense of smell.

Wiltschko is now the CEO of Osmo, a Google Research spinout based in Cambridge, Massachusetts. In September 2022, the company hit the ground running with $60 million in initial funding, including an investment from the Amazon Alexa Fund.

In the short term, Osmo aims to unlock a new era of commercial fragrance innovation. Longer term, the company envisions its technologies having the potential to save lives through the development of better insect repellents and even digital diagnostic tools for detecting serious illnesses on a person's breath.

The Principal Odor Map

The keystone to all this is the team’s breakthrough advance: the creation of what it calls the Principal Odor Map (POM).

Before vision could be digitized, a map called RGB was required: It shows how every color is made up of varying proportions of red, green, and blue. Before Osmo was spun out, Wiltschko’s team did something similar — and remarkable — with odor. They used machine learning to map the structure of a molecule directly to how humans perceive the smell of that molecule. In other words, they built a model that can tell you what a molecule smells like just by looking at it. This is the POM.

That was an ‘a-ha!’ moment for us, akin to passing a Turing test for odor. We'd built something with real commercial value that was sufficiently validated to bring into the world.
Jon Hennek

Here’s how they created POM and, crucially, how they proved it worked. They first trained a graph neural network (GNN) on about 5,000 molecules from several flavor and fragrance databases. The smells of all these molecules were well-documented with multiple human-judged odor labels such as beefy, floral, or minty. From this, the model was able to learn connections between molecular structure and odor, without needing any knowledge of what actually happens in the nose or brain of a person sniffing an odor.

That’s great, as far as it goes. The crucial question then was, could POM generalize to predict the smell of molecules it had never seen before, based solely on their molecular structure? And could it do that as well as trained human raters, which is the gold standard for odor characterization? To find out, the team took a diverse set of more than 400 odor molecules previously unseen by POM and had the model blindly predict their characteristics. Then a panel of trained human raters sniffed and labeled those same odors.

When the Osmo team compared the results, they were delighted. Not only had the model successfully predicted the odor of these unseen molecules as well as trained humans, but its predicted odor profiles were closer to the average results of the panel than any of the individual panelists themselves.

“That was an ‘a-ha!’ moment for us, akin to passing a Turing test for odor,” says Jon Hennek, chief product officer at Osmo. “We'd built something with real commercial value that was sufficiently validated to bring into the world.”

Islands of odor

POM is not a map in the typical sense, but it can nevertheless be compared to the RGB map. Pick two points at random on a two-dimensional color map. The closer those two points are to each other, the more similar the color. The same is true for odors in POM, though this map exists in a mind-bending 256 dimensions. All of the tulip-smelling molecules are close to each other, for example. Ditto for the brandy-smelling molecules.

“Zooming out a little, all the flowers are next to each other. There's a whole floral Pangaea in this odor map! We didn't tell it to do that,” says Wiltschko. This sort of grouping is also true for woods, bakery-type smells, alcoholic smells, you name it. Our brain seems to organize smells in nested hierarchies, says Wiltschko, so the rose odor is inside the rose category, inside the flowers category, inside the plants category, inside the pleasant category.

“The fact that we were able to observe this in the POM without telling it is astounding,” he says.

On the left is a color map (the CIE 1931 color space chromaticity diagram), similar colors lie near each other. On the right is Osmo’s Principal Odor Map, individual molecules (grey points) are found nearer to each other if they are predicted to smell similar.
In this color map (the CIE 1931 color space chromaticity diagram), similar colors lie near each other. Likewise, in Osmo’s Principal Odor Map, individual molecules (grey points) are found nearer to each other if they are predicted to smell similar.
Courtesy of Osmo

While Wiltschko has bold ideas for the future of Osmo’s technology, the first order of business is putting the company on a solid commercial footing. For now, Osmo is concentrating on developing new ingredients for the global fragrance category.

The Osmo team is using POM to explore the world of odor molecules — several billion of them — and homing in on molecules that POM predicts to have an interesting and strong olfactory character.

“We're much better at that, I believe, than anybody else in the world,” says Hennek. “Because rather than start with rules of thumb and chemical intuition, we are starting with an odor prediction for every molecule we could possibly synthesize. It lets us find molecules that a chemist might never have considered.”

The team is working with advisors, including Christophe Laudamiel, a French master perfumer, and potential customers include fragrance houses and packaged goods companies.

More from Alexa Fund
Alexa Fund company’s assisted reality tech could unlock speech for hundreds of millions of people who struggle to communicate.

“We've had repeated feedback that our ingredients have the potential to be very successful, commercially,” says Wiltschko. “That smells like product/market fit.” The principal idea is to license those molecules to fragrance houses.

It’s a timely endeavor. The global fragrance category is valued at more than $10 billion and growing steadily. But some traditional ingredients, such as sandalwood oils, can result in over-harvesting or other ecological harm, while the characteristics of other ingredients increasingly fall short as the demand grows for safer, more biodegradable products.

With POM, Osmo is paving the way for palettes of safe, synthetic fragrances that recreate natural odors using environmentally friendly and easily synthesized molecules. To that end Osmo is looking at combinations of just a handful of atoms: carbon, hydrogen, oxygen, nitrogen, phosphorus, and sulfur.

“Then we bring them into our lab for a process akin to a drug discovery pipeline,” says Hennek. “We are working towards regulatory approval of those molecules.”

Rise of the graph neural networks

All of this has only become possible in the last six years or so. The core insight that started this scientific project, says Wiltschko, was that machine learning was “getting really good at molecules,” thanks to the recent rise of GNNs.

Related content
Dual embeddings of each node, as both source and target, and a novel loss function enable 30% to 160% improvements over predecessors.

Previously, machine learning approaches primarily converted inputs — images or data arrays, say — into rectangles or data grids to process them. Molecules didn’t fit this mold: a molecule might be two atoms, or it might be 20 atoms, with wildly different structure and connectivity. They are simply not reducible to rectangles or grids.

Instead, the atoms in a molecule can be considered as nodes, and the chemical bonds between them as edges, forming a graph structure. This representation allows GNNs to model and process molecular data.

“Some of this technology was developed by friends of mine at Google. So, it was a fantastic, fertile ground to start exploring this idea,” says Wiltschko.

This ongoing exploration is creating some exciting possibilities. Wiltschko reasoned that, just as the sun has shone on Earth since before life began, resulting in many creatures evolving similar visual apparatuses, the composition of the Earth’s atmosphere has been broadly stable over evolutionary time. So could POM also be used to understand the olfactory responses of other species, even those separated from humans by millions of years of evolution?

Life-saving potential

Take mosquitos. Could POM be used to work out what odors repel these disease-carrying insects?

To find out, they augmented POM with additional data sources. The first was a long-forgotten U.S. government report, published in the 1940s, that featured the results of testing 19,000 compounds for their mosquito repellency. The second was information provided by TropIQ, a Dutch company that develops malaria-control technology. The augmented model was soon able to predict entirely new molecules with repellency at least as powerful as DEET, the active ingredient in the most effective mosquito repellents.

osmo image 2.png
Osmo digitized mosquito-repellency data for 19,000 compounds reported on by the United States Department of Agriculture and used that to refine its model (left). The team then predicted candidate molecules that would be most repellent to mosquitos, produced the most viable options, tested them on real mosquitos, and fed those results back into the model to further refine it.
Courtesy of Osmo

The development of cheaper, more effective, and safer insect repellents could have a huge impact on global health. Wiltschko has nothing to announce yet, but says this research is ongoing in collaboration with the Bill & Melinda Gates Foundation.

Applying POM to mosquitos is also a proof of concept, says Hennek. “We can picture applying our product not just to what mosquitoes don’t like, but to what roaches don’t like. Or any number of agricultural pests.”

Capturing smell forever

Looking further down the road, Wiltschko’s vision is to digitize our sense of smell. The idea is not as far-fetched as it sounds. Consider several hundred years ago. The idea that a visual moment — the fleeting expression on your child’s face or an orchard of apple trees in blossom — could be instantly captured and made available forever more in perfect color would have been nothing short of magical thinking.

By the 1820s came the first photography, and with it, the first steps towards human mastery of the world of light. Today, it feels like a fundamental right to freeze those visual memories and hold on to them forever. And the same goes for the auditory world.

“We know what’s required to digitize a human sense,” says Wiltschko. “And we don't have to wait for any of the inventions that vision did — particularly integrated circuits.”

Indeed, with modern computing power and the harnessing of machine learning, Wiltschko reckons computers will have a “sense of smell” within a decade or two. Three stages are required: “reading” smell, understanding it, and “writing” it. Osmo wants to understand, and ultimately curate a wide palette of safe, synthetic molecules that can recreate the entire human smellscape. The reading (sensing) of odorous molecules currently requires bulky and expensive lab equipment, such as a gas chromatography mass spectrometer, while the writing (producing) of smells on demand remains science fiction at the consumer level, says Wiltschko, for now.

A window to the inside

Sensing and understanding odor at a high level may be sufficient to herald powerful health applications, says Wiltschko. For example, it is well established that serious illnesses, including some cancers, can be detected through their effect on your breath. Being able to take a snapshot of that odor profile — an “Osmograph”, in Wiltschko’s words – could reveal a great deal about what’s going on inside our bodies.

“We don't know if that technology will ultimately have a transformative effect on healthcare, but I am betting that it will,” he says.

Related content
ARA recipient Marinka Zitnik is focused on how machine learning can enable accurate diagnoses and the development of new treatments and therapies.

It’s very important to Wiltschko that, down the line, Osmo grows to develop clinical diagnostics applications. “That's the North Star for me, and it's very important that we get there. But the sheer cost and the talent that's required is rare and expensive,” he says. “So, it can’t be the first beach that we storm.”

As Osmo grows, it will be looking for similarly passionate people to push the mission forward. “We've been finding that there are people out there who are secret scent lovers, who secretly aspire to work in the field of machine olfaction,” says Wiltschko. “Just to put it out there: there's one place to do this, and it's Osmo.”

Talking to Wiltschko and those inspired to work alongside him, it is clear to see that Osmo is the culmination of his lifelong passions. For him, it’s emotional. “Once you smell a thing, you cannot stop the feelings that you get from it. There's a very fundamental feeling and emotional component,” he says, “and I think that’s beautiful.”

Research areas

Related content

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 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, 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 - 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
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Seattle office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.
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.
GB, London
Are you looking to work at the forefront of Machine Learning and AI? Would you be excited to apply cutting edge Generative AI algorithms to solve real world problems with significant impact? The AWS Industries Team at AWS helps AWS customers implement Generative AI solutions and realize transformational business opportunities for AWS customers in the most strategic industry verticals. This is a team of data scientists, engineers, and architects working step-by-step with customers to build bespoke solutions that harness the power of generative AI. The team helps customers imagine and scope the use cases that will create the greatest value for their businesses, select and train and fine tune the right models, define paths to navigate technical or business challenges, develop proof-of-concepts, and build applications to launch these solutions at scale. The AWS Industries team provides guidance and implements best practices for applying generative AI responsibly and cost efficiently. You will work directly with customers and innovate in a fast-paced organization that contributes to game-changing projects and technologies. You will design and run experiments, research new algorithms, and find new ways of optimizing risk, profitability, and customer experience. In this Data Scientist role you will be capable of using GenAI and other techniques to design, evangelize, and implement and scale cutting-edge solutions for never-before-solved problems. Key job responsibilities - Collaborate with AI/ML scientists, engineers, and architects to research, design, develop, and evaluate cutting-edge generative AI algorithms and build ML systems to address real-world challenges - Interact with customers directly to understand the business problem, help and aid them in implementation of generative AI solutions, deliver briefing and deep dive sessions to customers and guide customer on adoption patterns and paths to production - Create and deliver best practice recommendations, tutorials, blog posts, publications, sample code, and presentations adapted to technical, business, and executive stakeholder - Provide customer and market feedback to Product and Engineering teams to help define product direction About the team Diverse Experiences Amazon values diverse experiences. Even if you do not meet all of the preferred 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 AWS Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. 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 we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship and 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, mentorship and other career-advancing resources here to help you develop into a better-rounded professional.