Zoox 3D map.gif
This visualization shows a Zoox vehicle aligning lidar data to Zoox's 3D map to localize itself in downtown San Francisco. Central to the Zoox navigation system is a cluster of capabilities: calibration, localization, and mapping.

How Zoox vehicles “find themselves” in an ever-changing world

Advanced machine learning systems help autonomous vehicles react to unexpected changes.

For a human to drive successfully around an urban environment, they must be able to trust their eyes and other senses, know where they are, understand the permissible ways to move their vehicle safely, and of course know how to reach their destination.

Building these abilities, and so many more, into an autonomous electric vehicle designed to transport customers smoothly and safely around densely populated cities takes an astonishing amount of technological innovation. Since its founding in 2014, Zoox has been developing autonomous ride-hailing vehicles, and the systems that support them, from the ground up. The company, which is based in Foster City, California, became an independent subsidiary of Amazon in 2020.

Zoox Fully Autonomous Vehicle at Coit Tower San Francsico
The Zoox L5 fully autonomous, all-electric robotaxi has no forward or backward, can reach speeds of up to 75 miles per hour, and can move all four wheels independently.

The Zoox purpose-built robot is an autonomous, pod-like electric vehicle that can carry four passengers in comfort. It has no forward or backward, being equally happy to drive in either direction, at up to 75 miles per hour, and can move all four wheels independently. There are no manual driving controls inside the vehicle.

Zoox has already done a great deal of testing of its autonomous driving systems using a fleet of retrofitted Toyota Highlander vehicles — with a human driver at the wheel, ready to take over if needed — in San Francisco, Las Vegas, Foster City, and Seattle.

Central to the Zoox navigation system is a cluster of capabilities called calibration, localization, and mapping. Only through this combination of abilities can Zoox vehicles understand their environment with exquisite precision, know where they are in relation to everything in their vicinity and beyond, and know exactly where they are going.

Zoox test vehicles, in this instance Toyota Highlanders, are retrofitted with an almost identical sensor configuration and compute system to their purpose-built vehicle.
Zoox has already done a great deal of testing of its autonomous driving systems using a fleet of Toyota Highlanders retrofitted with an almost identical sensor configuration and compute system to the purpose-built vehicle — with a human driver at the wheel, ready to take over if needed — in San Francisco, Las Vegas, Foster City, and Seattle.

This is the domain of Zoox’s CLAMS (Calibration, Localization, and Mapping Simultaneously) and Zoox Road Network (ZRN) teams, which together enable the vehicle to meaningfully understand its location and process its surroundings. To get an idea of how these elements work in concert, Amazon Science spoke to several members of these teams.

In terms of awareness of its environment, the Zoox vehicle can fairly be likened to an all-seeing eye. Its state-of-the-art sensor architecture is made up of LiDARs (Light Detection and Ranging), radars, visual cameras, and longwave-infrared cameras. These are arrayed symmetrically around the outside of the vehicle, providing an overlapping, 360-degree field of view.

With this many sensors in play, it is critical that their input is stitched together accurately to create a true and self-consistent picture of everything happening all around the vehicle, moment to moment. To do that, the vehicle needs to know exactly where its sensors are in relation to each other, and with sensors of such high resolution, it’s not enough simply to know where the sensors were attached to the vehicle in the first place.

“To a very minor but still important degree, every vehicle is a special snowflake in some way,” says Taylor Arnicar, staff technical program manager, who oversees the CLAMS and ZRN teams. “And the other reality is we’re exposing these vehicles to rather harsh real-world conditions. There’s shock and vibe, thermal events, and all these things can cause very slight changes in sensor positioning.” Were such changes to be ignored, it could result in unacceptably “blurry” vision, Arnicar says.

In other autonomous-robotics applications, sensor calibration typically entails the robot looking at a specific calibration target, displayed on surrounding infrastructure, such as a wall. With the Zoox vehicle destined for the ever-changing urban environment, the Zoox team is pioneering infrastructure-free calibration.

This animation shows a Zoox system aligning color camera edges to lidar depth edges
This animation shows a Zoox system aligning color camera edges to lidar depth edges. With the Zoox vehicle destined for the ever-changing urban environment, the Zoox team is pioneering infrastructure-free calibration.

“That means we rely on the natural environment — whatever objects, shapes, and colors are in the world around the vehicle as it drives,” says Arnicar. One way the team does this is by automatically identifying image gradients — such as the edges of buildings or the trunks of trees — from the vehicle’s color camera data and aligning those with depth edges in the LiDAR data.

It is worth emphasizing that a superpower of the Zoox vehicle is seeing its surroundings with superhuman perception. With so many sensors mounted externally, in pods on the corners of the vehicles, it can see what’s coming around every corner before a human driver would. Its LiDARs and visual cameras mean it knows what lies in every direction with high precision. It even boasts a kind of X-ray vision: “Certain materials don’t obstruct the radar,” says Elena Strumm, Zoox’s engineering manager for mapping algorithms. “When a bicyclist is cycling behind a bush, for example, we might get a really clear radar signature on them, even if that bush has occluded the LiDAR and visual cameras.”

Related content
Jesse Levinson, co-founder and CTO of Zoox, answers 3 questions about the challenges of developing autonomous vehicles and why he’s excited about Zoox’s robotaxi fleet.

Now that the vehicle can rely on what it senses, it needs a map. The Zoox team gathers its map data first-hand by driving around the cities in which it will operate in Toyota Highlanders retrofitted with the full Zoox sensor architecture. LiDAR data and visual images collected in this way can be made into high-definition maps by the CLAMS team. But first, all the people, cars, and other ephemeral aspects of the urban landscape must be removed from the LiDAR data. For this, machine learning is required.

When the Zoox vehicle is in normal urban operations, it is fundamental that its perception system recognizes the aspects of incoming LiDAR data that represent pedestrians, cyclists, cars or trucks — or indeed anything that may move in ways that need to be anticipated. LiDARs create enormous amounts of information about the dynamic 3D environment around the vehicle in the form of “point clouds” — sets of points that describe the objects and surfaces visible to the LiDAR. Using machine learning to instantly identify people in a fast-moving, dynamic environment is a challenge, particularly as people may be moving, static, partly occluded, in a wheelchair, only visible from the knees down, or any number of possibilities.

A raw lidar point cloud of Caesars Palace in Las Vegas, before it’s turned into an efficient mesh representation for the 3D map.
A raw lidar point cloud of Caesars Palace in Las Vegas, before it’s turned into an efficient mesh representation for the 3D map.

“Machine-learned AI systems excel at this kind of pattern-matching problem. You feed millions of examples of something and then they can do a great good job of recognizing that thing in the abstract,” Arnicar explains.

In a beautiful piece of synergy, the Zoox mapping team benefits from this safety-critical application of machine learning because they require the reverse information — they want to take the people and cars out of the data so that they can create 3D maps of the road landscape and infrastructure alone.

“Once these elements are identified and removed from the mapping data, it becomes possible to combine LiDAR-based point clouds from overlapping locations to create high resolution 3D maps,” says Strumm.

But maps are not useful to the vehicle without meaning. To create a “semantic map,” the ZRN team adds layers of information to the 3D map that encode everything static that the vehicle needs to navigate the road safely, including speed limits, traffic light locations, one-way streets, keep-clear zones and more.

Related content
Deep learning to produce invariant representations, estimations of sensor reliability, and efficient map representations all contribute to Astro’s superior spatial intelligence.

The final core piece of the CLAMS team’s work is localization. Zoox’s localization technology allows each vehicle to know where it is in the world — and on its map — to within a few centimeters, and its direction to within a fraction of a degree. The vehicle does this not only by comparing its visual inputs with its map, but also by utilizing GPS, accelerometers, wheel speeds, gyroscopes, and more. It can therefore check its precise location and velocity hundreds of times per second. Armed with a combination of the physical and semantic maps, and always aware of its place in relation to every object or person in its vicinity, the vehicle can navigate safely to its destination.

Part of the localization challenge is that any map will become dated over time, Arnicar explains. “Once you build the map — from the moment the data is collected — you need to consider that it could be out of date.” This is because the world can change anytime, anywhere, without notice. “On one occasion one of our Toyota Highlanders was driving down the street collecting data, and right in front of us was a construction truck with a guy hanging off the back, repainting the lane line in a different place as they drove along. No amount of fast mapping can catch up with these sorts of scenarios.” In practice, this means the map needs to be treated as a guidebook for the vehicle, not as gospel.

“This changeability of the real world led us to create the ZRN Monitor, a system on the vehicle that determines whether the actual road environment has differed from our semantic map data,” says Chris Gibson, engineering manager for the Zoox Road Network team. “For example, if lane markings have changed and now the double yellow lines have moved, then if we don’t detect that dynamically, we could potentially end up driving into opposing traffic. From a safety perspective, we must make absolutely certain that the vehicle does not drive into such areas.” The ZRN Monitor’s role is to identify and, to an extent, evaluate the safety implications of such unanticipated environmental modifications. These notifications are also an indication that it may be time to update the map for that area with more recent sensor data.

In the uncommon situation in which the vehicle encounters a challenging driving situation and it isn’t highly confident of a safe way to proceed, it can request “TeleGuidance” — a human operator located in a dedicated service center is provided with the full 3D understanding of the vehicle’s environment, as well as live-streamed sensor data.

A Zoox TeleGuidance tactician providing remote guidance to a vehicle from the Zoox HQ
A Zoox TeleGuidance tactician provides remote guidance to a vehicle from Zoox HQ.

“Imagine a construction zone. The Zoox vehicle might need to be directed to drive on the other side of the road, which would normally carry oncoming traffic. That’s a rule that under most circumstances you shouldn’t break, but in this instance, a TeleGuidance tactician might provide the robot with waypoints to ensure it knows where it needs to go in that moment,” says Gibson. The vehicle remains responsible for the safety of its passengers, however, and continues to drive autonomously at all times while acting on the TeleGuidance information.

Before paying customers will be able to use their smartphones to hail a Zoox vehicle, more on-road testing first needs to be done. Zoox has built dozens of its purpose-built vehicles and is testing them on “semi-private courses” in California, according to Zoox’s co-founder and chief technology officer, Jesse Levinson. Next on the agenda is full testing on public roads, says Levinson, who promises that is “really not that far away. We’re not talking about years.”

So, what does it feel like to be transported in a Zoox vehicle?

“I’ve ridden in a Zoox vehicle, with no safety driver, no steering wheel, no anything — just me in the vehicle,” says Arnicar. “And it is magical. It’s what I’ve been working at Zoox seven years to experience. I’ve seen Zoox go from sketches on a napkin to something I can ride in. That's pretty amazing.”

When an autonomous Zoox vehicle ultimately comes around a corner near you, know this for a fact: no matter how striking and novel it looks, it will see you before you see it.

Research areas

Related content

US, WA, Seattle
Job description: We are reimagining Amazon Search with an interactive conversational experience that helps you find answers to product questions, perform product comparisons, receive personalized product suggestions, and so much more, to easily find the perfect product for your needs. We’re looking for the best and brightest across Amazon to help us realize and deliver this vision to our customers right away. This will be a once in a generation transformation for Search, just like the Mosaic browser made the Internet easier to engage with three decades ago. If you missed the 90s—WWW, Mosaic, and the founding of Amazon and Google—you don’t want to miss this opportunity.
US, WA, Bellevue
We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, (Bayesian) time series, macroeconomic, as well as basic familiarity with Matlab, R, or Python is necessary, and experience with SQL would be a plus. These are full-time positions at 40 hours per week, with compensation being awarded on an hourly basis. You will learn how to build data sets and perform applied econometric analysis at Internet speed collaborating with economists, scientists, and product managers. These skills will translate well into writing applied chapters in your dissertation and provide you with work experience that may help you with placement. Roughly 85% of previous cohorts have converted to full time economics employment at Amazon. If you are interested, please send your CV to our mailing list at econ-internship@amazon.com.
US, WA, Seattle
Do you want to join an innovative team of scientists who use machine learning to help Amazon provide the best experience to our Selling Partners by automatically understanding and addressing their challenges, needs and opportunities? Do you want to build advanced algorithmic systems that are powered by state-of-art ML, such as Natural Language Processing, Large Language Models, Deep Learning, Computer Vision and Causal Modeling, to seamlessly engage with Sellers? Are you excited by the prospect of analyzing and modeling terabytes of data and creating cutting edge algorithms to solve real world problems? Do you like to build end-to-end business solutions and directly impact the profitability of the company and experience of our customers? Do you like to innovate and simplify? If yes, then you may be a great fit to join the Selling Partner Experience Science team. Key job responsibilities Use statistical and machine learning techniques to create the next generation of the tools that empower Amazon's Selling Partners to succeed. Design, develop and deploy highly innovative models to interact with Sellers and delight them with solutions. Work closely with teams of scientists and software engineers to drive real-time model implementations and deliver novel and highly impactful features. Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation. Research and implement novel machine learning and statistical approaches. Lead strategic initiatives to employ the most recent advances in ML in a fast-paced, experimental environment. Drive the vision and roadmap for how ML can continually improve Selling Partner experience. About the team Selling Partner Experience Science (SPeXSci) is a growing team of scientists, engineers and product leaders engaged in the research and development of the next generation of ML-driven technology to empower Amazon's Selling Partners to succeed. We draw from many science domains, from Natural Language Processing to Computer Vision to Optimization to Economics, to create solutions that seamlessly and automatically engage with Sellers, solve their problems, and help them grow. Focused on collaboration, innovation and strategic impact, we work closely with other science and technology teams, product and operations organizations, and with senior leadership, to transform the Selling Partner experience.
US, WA, Seattle
The AWS AI Labs team has a world-leading team of researchers and academics, and we are looking for world-class colleagues to join us and make the AI revolution happen. Our team of scientists have developed the algorithms and models that power AWS computer vision services such as Amazon Rekognition and Amazon Textract. As part of the team, we expect that you will develop innovative solutions to hard problems, and publish your findings at peer reviewed conferences and workshops. AWS is the world-leading provider of cloud services, has fostered the creation and growth of countless new businesses, and is a positive force for good. Our customers bring problems which will give Applied Scientists like you endless opportunities to see your research have a positive and immediate impact in the world. You will have the opportunity to partner with technology and business teams to solve real-world problems, have access to virtually endless data and computational resources, and to world-class engineers and developers that can help bring your ideas into the world. Our research themes include, but are not limited to: few-shot learning, transfer learning, unsupervised and semi-supervised methods, active learning and semi-automated data annotation, large scale image and video detection and recognition, face detection and recognition, OCR and scene text recognition, document understanding, 3D scene and layout understanding, and geometric computer vision. For this role, we are looking for scientist who have experience working in the intersection of vision and language. We are located in Seattle, Pasadena, Palo Alto (USA) and in Haifa and Tel Aviv (Israel).
RO, Iasi
Amazon’s mission is to be earth’s most customer-centric company and our team is the guardian of our customer’s privacy. Amazon SDO Privacy engineering operates in Austin – TX, US and Iasi, Bucharest – Romania. Our mission is to develop services which will enable every Amazon service operating with personal data to satisfy the privacy rights of Amazon customers. We are working backwards from the customers and world-wide privacy regulations, think long term, and propose solutions which will assure Amazon Privacy compliance. Our external customers are world-wide customers of Amazon Retail Website, Amazon B2B services (e.g. Seller central, App / Skill Developers), and Amazon Subsidiaries. Our internal customers are services within Amazon who operate with personal data, Legal Representatives, and Customer Service Agents. You can opt-in for being part of one of the existing or newly formed engineering teams who will contribute to Amazon mission to meet external customers’ privacy rights: Personal Data Classification, The Right to be forgotten, The right of access, or Digital Markets Act – The Right of Portability. The ideal candidate has a great passion for data and an insatiable desire to learn and innovate. A commitment to team work, hustle and strong communication skills (to both business and technical partners) are absolute requirements. Creating reliable, scalable, and high-performance products requires a sound understanding of the fundamentals of Computer Science and practical experience building large-scale distributed systems. Your solutions will apply to all of Amazon’s consumer and digital businesses including but not limited to Amazon.com, Alexa, Kindle, Amazon Go, Prime Video and more. Key job responsibilities As an data scientist on our team, you will apply the appropriate technologies and best practices to autonomously solve difficult problems. You'll contribute to the science solution design, run experiments, research new algorithms, and find new ways of optimizing customer experience. Besides theoretical analysis and innovation, you will work closely with talented engineers and ML scientists to put your algorithms and models into practice. You will collaborate with partner teams including engineering, PMs, data annotators, and other scientists to discuss data quality, policy, and model development. Your work will directly impact the trust customers place in Amazon Privacy, globally.
JP, 13, Tokyo
The JP Economics team is a central science team working across a variety of topics in the JP Retail business and beyond. We work closely with JP business leaders to drive change at Amazon. We focus on solving long-term, ambiguous and challenging problems, while providing advisory support to help solve short-term business pain points. Key topics include pricing, product selection, delivery speed, profitability, and customer experience. We tackle these issues by building novel economic/econometric models, machine learning systems, and high-impact experiments which we integrate into business, financial, and system-level decision making. Our work is highly collaborative and we regularly partner with JP- EU- and US-based interdisciplinary teams. In this role, you will build ground-breaking, state-of-the-art causal inference models to guide multi-billion-dollar investment decisions around the global Amazon marketplaces. You will own, execute, and expand a research roadmap that connects science, business, and engineering and contributes to Amazon's long term success. As one of the first economists outside North America/EU, you will make an outsized impact to our international marketplaces and pioneer in expanding Amazon’s economist community in Asia. The ideal candidate will be an experienced economist in empirical industrial organization, labour economics, econometrics, or related structural/reduced-form causal inference fields. You are a self-starter who enjoys ambiguity in a fast-paced and ever-changing environment. You think big on the next game-changing opportunity but also dive deep into every detail that matters. You insist on the highest standards and are consistent in delivering results. Key job responsibilities Work with Product, Finance, Data Science, and Data Engineering teams across the globe to deliver data-driven insights and products for regional and world-wide launches. Innovate on how Amazon can leverage data analytics to better serve our customers through selection and pricing. Contribute to building a strong data science community in Amazon Asia.
GB, London
Are you excited about applying economic models and methods using large data sets to solve real world business problems? Then join the Economic Decision Science (EDS) team. EDS is an economic science team based in the EU Stores business. The teams goal is to optimize and automate business decision making in the EU business and beyond. An internship at Amazon is an opportunity to work with leading economic researchers on influencing needle-moving business decisions using incomparable datasets and tools. It is an opportunity for PhD students and recent PhD graduates in Economics or related fields. We are looking for detail-oriented, organized, and responsible individuals who are eager to learn how to work with large and complicated data sets. Knowledge of econometrics, as well as basic familiarity with Stata, R, or Python is necessary. Experience with SQL would be a plus. As an Economics Intern, you will be working in a fast-paced, cross-disciplinary team of researchers who are pioneers in the field. You will take on complex problems, and work on solutions that either leverage existing academic and industrial research, or utilize your own out-of-the-box pragmatic thinking. In addition to coming up with novel solutions and prototypes, you may even need to deliver these to production in customer facing products. Roughly 85% of previous intern cohorts have converted to full time economics employment at Amazon.
US, CA, Cupertino
We're looking for an Applied Scientist to help us secure Amazon's most critical data. In this role, you'll work closely with internal security teams to design and build AR-powered systems that protect our customers' data. You will build on top of existing formal verification tools developed by AWS and develop new methods to apply those tools at scale. You will need to be innovative, entrepreneurial, and adaptable. We move fast, experiment, iterate and then scale quickly, thoughtfully balancing speed and quality. Inclusive Team Culture Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Work/Life Balance Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives. Mentorship & Career Growth Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. Our senior members enjoy one-on-one mentoring and thorough, but kind, code reviews. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded engineer and enable them to take on more complex tasks in the future. Key job responsibilities Deeply understand AR techniques for analyzing programs and other systems, and keep up with emerging ideas from the research community. Engage with our customers to develop understanding of their needs. Propose and develop solutions that leverage symbolic reasoning services and concepts from programming languages, theorem proving, formal verification and constraint solving. Implement these solutions as services and work with others to deploy them at scale across Payments and Healthcare. Author papers and present your work internally and externally. Train new teammates, mentor others, participate in recruiting and interviewing, and participate in our tactical and strategic planning. About the team Our small team of applied scientists works within a larger security group, supporting thousands of engineers who are developing Amazon's payments and healthcare services. Security is a rich area for automated reasoning. Most other approaches are quite ad-hoc and take a lot of human effort. AR can help us to reason deliberately and systematically, and the dream of provable security is incredibly compelling. We are working to make this happen at scale. We partner closely with our larger security group and with other automated reasoning teams in AWS that develop core reasoning services.
US, NY, New York
Search Thematic Ad Experience (STAX) team within Sponsored Products is looking for a leader to lead a team of talented applied scientists working on cutting-edge science to innovate on ad experiences for Amazon shoppers!. You will manage a team of scientists, engineers, and PMs to innovate new widgets on Amazon Search page to improve shopper experience using state-of-the-art NLP and computer vision models. You will be leading some industry first experiences that has the potential to revolutionize how shopping looks and feels like on Amazon, and e-commerce marketplaces in general. You will have the opportunity to design the vision on how ad experiences look on Amazon search page, and use the combination of advanced techniques and continuous experimentation to realize this vision. Your work will be core to Amazon’s advertising business. You will be a significant contributor in building the future of sponsored advertising, directly impacting the shopper experience for our hundreds of millions of shoppers worldwide, while delivering significant value for hundreds of thousands of advertisers across the purchase journey with ads on Amazon. Key job responsibilities * Be the technical leader in Machine Learning; lead efforts within the team, and collaborate and influence across the organization. * Be a critic, visionary, and execution leader. Invent and test new product ideas that are powered by science that addresses key product gaps or shopper needs. * Set, plan, and execute on a roadmap that strikes the optimal balance between short term delivery and long term exploration. You will influence what we invest in today and tomorrow. * Evangelize the team’s science innovation within the organization, company, and in key conferences (internal and external). * Be ruthless with prioritization. You will be managing a team which is highly sought after. But not all can be done. Have a deep understanding of the tradeoffs involved and be fierce in prioritizing. * Bring clarity, direction, and guidance to help teams navigate through unsolved problems with the goal to elevate the shopper experience. We work on ambiguous problems and the right approach is often unknown. You will bring your rich experience to help guide the team through these ambiguities, while working with product and engineering in crisply defining the science scope and opportunities. * Have strong product and business acumen to drive both shopper improvements and business outcomes. A day in the life * Lead a multidisciplinary team that embodies “customer obsessed science”: inventing brand new approaches to solve Amazon’s unique problems, and using those inventions in software that affects hundreds of millions of customers * Dive deep into our metrics, ongoing experiments to understand how and why they are benefitting our shoppers (or not) * Design, prototype and validate new widgets, techniques, and ideas. Take end-to-end ownership of moving from prototype to final implementation. * Be an advocate and expert for STAX science to leaders and stakeholders inside and outside advertising. About the team We are the Search thematic ads experience team within Sponsored products - a fast growing team of customer-obsessed engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives to drive value for both our customers and advertisers, through continuous innovation. We focus on new ads experiences globally to help shoppers make the most informed purchase decision while helping shortcut the time to discovery that shoppers are highly likely to engage with. We also harvest rich contextual and behavioral signals that are used to optimize our backend models to continually improve the shopper experience. We obsess about our customers and are continuously seeking opportunities to delight them.
US, CA, Palo Alto
Amazon is the 4th most popular site in the US. Our product search engine, one of the most heavily used services in the world, indexes billions of products and serves hundreds of millions of customers world-wide. We are working on a new initiative to transform our search engine into a shopping engine that assists customers with their shopping missions. We look at all aspects of search CX, query understanding, Ranking, Indexing and ask how we can make big step improvements by applying advanced Machine Learning (ML) and Deep Learning (DL) techniques. We’re seeking a thought leader to direct science initiatives for the Search Relevance and Ranking at Amazon. This person will also be a deep learning practitioner/thinker and guide the research in these three areas. They’ll also have the ability to drive cutting edge, product oriented research and should have a notable publication record. This intellectual thought leader will help enhance the science in addition to developing the thinking of our team. This leader will direct and shape the science philosophy, planning and strategy for the team, as we explore multi-modal, multi lingual search through the use of deep learning . We’re seeking an individual that can enhance the science thinking of our team: The org is made of 60+ applied scientists, (2 Principal scientists and 5 Senior ASMs). This person will lead and shape the science philosophy, planning and strategy for the team, as we push into Deep Learning to solve problems like cold start, discovery and personalization in the Search domain. Joining this team, you’ll experience the benefits of working in a dynamic, entrepreneurial environment, while leveraging the resources of Amazon [Earth's most customer-centric internet company]. We provide a highly customer-centric, team-oriented environment in our offices located in Palo Alto, California.