Jesse Levinson, co-founder and CTO of Zoox
Jesse Levinson, co-founder and CTO of Zoox, completed his PhD and postdoc under Sebastian Thrun at Stanford. He developed algorithms for Stanford’s entry in the 2007 DARPA Urban Challenge and went on to lead the self-driving team’s research and development efforts.
Zoox

The future of mobility-as-a-service

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.

In June 2020, Amazon acquired Zoox, a then six-year-old California-based startup focused on “creating autonomous mobility from the ground up.”

Six months later, Zoox, now an independent Amazon subsidiary, shared publicly for the first time a look at its electric, autonomous vehicle created for dense, urban environments. The vehicle reveal marked a key milestone toward the organization’s vision of creating an autonomous robotaxi fleet and ride-hailing service designed with passengers in mind.

At its unveiling in December 2020, Zoox CEO Aicha Evans said her team is transforming the rider experience to provide superior “mobility-as-a-service” for customers. Moreover, she added, given the current data related to carbon emissions and traffic accidents, “It’s more important than ever that we build a sustainable, safe solution that allows riders to get from point A to point B.”

See how a Zoox robotaxi traverses city streets.

Jesse Levinson, co-founder and chief technology officer of Zoox, guides the company’s technology roadmap and execution to turn its mobility-as-a-service vision into reality. After graduating summa cum laude from Princeton, he completed his PhD and postdoc under Sebastian Thrun at Stanford. There, he developed algorithms for Stanford’s successful entry in the 2007 DARPA Urban Challenge and went on to lead the self-driving team’s research and development efforts.

Amazon Science asked Levinson about the challenges of developing self-driving vehicles and why he’s excited about Zoox’s approach.

Q. You were one of the authors on the 2008 paper, Junior: The Stanford Entry in the Urban Challenge. That race was a closed-course competition, and not quite representative of real-world challenges. But what key observations did you take away from that experience?

Probably the most important realization after the race was the dichotomy of how much there was still left to solve and the fact that it was actually all going to be solvable. It’s quite easy to get enchanted with one or the other of those observations; either that the problem is practically impossible because of all the things that still aren’t perfect, or that it must be almost solved because of some super cool demo or milestone that seems incredibly impressive. The reality is in between, and for whatever reason, it’s surprisingly hard for people to maintain a nuanced appreciation of that balance.

Achieving a world with ubiquitous autonomous vehicles will be an incremental process that advances every year — and remember, the alternative is the bar of human performance that stays nearly stagnant.
Jesse Levinson

In 2004, DARPA held its first Grand Challenge:  a 125-mile race in the desert. Of the 20 teams that entered, none completed the race, and the best vehicle only completed about six miles. The industry (and the media) widely regarded the outcome as an abysmal failure of AI. Yet it was not a failure, but an incredible feat of engineering. If an autonomous vehicle can drive six miles in the desert all by itself, then it doesn’t take an incredible imagination to foresee it driving 125 miles.

Lo and behold, the very next year, six vehicles finished the full 125-mile course. It was a promising step towards the future, and a year later, in 2006, DARPA announced the Urban Challenge, which several teams completed successfully. Our entry at Stanford came in second place. Excited by the results, many people made overly optimistic predictions on the mass-adoption of self-driving cars, which were subsequently deflated by various challenges we’ve seen in the industry since that time.

It has been eye-opening to watch the public's reaction to self-driving cars over time. I have always tried my best to be upfront, honest, and realistic about where the technology is — and while I’ve certainly not nailed all of my predictions, I do think I’ve managed to be fairly balanced overall. As technologists, when we are overly optimistic or pessimistic, we do a disservice to ourselves, the industry, and our technology. Achieving a world with ubiquitous autonomous vehicles will be an incremental process that advances every year — and remember, the alternative is the bar of human performance that stays nearly stagnant. It’s the opportunity of a lifetime to participate in the journey of making autonomous driving technology relentlessly better. Soon, it will reach a crossover point where the public begins to adopt it at scale, which will be a transformative win for society at large.

Q. Following up on your answer, what did you learn from that experience that you apply to your current role at Zoox? Has your approach changed since that challenge or remained largely the same?

So much! I’m grateful for that experience because it was formative in the early approach of Zoox. Here’s some of the lessons I took away from it:

Zoox Autonomous Vehicle - Single Side - Coit Tower SF.png
Zoox notes is "the first in the industry to showcase a driving, purpose-built robotaxi capable of operating up to 75 miles per hour."
Zoox

First, teaching cars to drive will not take as long as we thought. In the early 2000s, we all thought it would be many, many decades before self-driving cars would be a reality. The DARPA challenge changed that. To build a vehicle that could navigate many realistic traffic scenarios only took about a year for a small team. Of course, there’s a huge difference between that and what’s required to operate an autonomous vehicle on public roads. But it was an important milestone that highlighted that autonomous driving technology could be a reality within a couple of decades.

Second, system integration and wide-scale testing is critical. No amount of knowledge about artificial intelligence, or anything else for that matter, will lead a mythical genius to intellectually divine a perfect solution. We need to combine and integrate many different complex systems and then see what works and fails through simulations, then closed courses, then public roads (with safety drivers). We have to test and experiment and iterate with massive data and scale, as opposed to trying to reason our way to a perfect solution.

On the other hand, blindly searching for progress without having any vision or architectural insights is also a bad idea; that’s one of the reasons why we identified the benefits of 270-degree sensing on all four corners of our ground-up vehicle at Zoox way back in 2014, a few years before we could drive autonomously in cities — because we knew from first principles that it was the right way to perceive the world.

Zoox Autonomous Vehicle - Reveal Sensor Detail.png
The Zoox vehicles utilize a unique sensor (some of which are seen here) architecture of cameras, radar, and LIDAR to obtain a 270-degree field of view on all four corners of the vehicle.
Zoox

Last, we have to test the various software and hardware components collectively to see how they respond to errors and uncertainty. By building a robust system that handles a cascading series of errors and ambiguities, you can explicitly track uncertainty and represent the state of the world more thoroughly. The proper representation of the world is not a singular, perfect model, but rather a distribution of probabilities and uncertainties. If you can design your system to be robust to imperfect sensor data, unpredictable agents, and unusual environments, you have a real shot at solving the problem in a world that’s not always the way you want it to be. It’s actually what humans do really well all the time, even though we’re rarely conscious that we’re doing it.

Q. You’ve said that safety is the foundation of everything Zoox does, and that the experience of building Zoox’s robotaxi has given you the opportunity to reimagine passenger safety. Can you give us insight into some of the systems you’ve developed for passenger safety, particularly the AI stack that underpins these efforts?

Yes, that’s right: safety is absolutely fundamental to the Zoox mission. With apologies for using an overused phrase, autonomous mobility allows for a paradigm shift (sorry!) in safety — from reactive to proactive. It’s an important point: automotive safety has always been reactive, focused on protecting vehicle occupants in crashes, which are seen to be inevitable. By building an autonomous vehicle from the ground-up, we can add a layer of proactive crash prevention that simply does not exist in today’s human-driven cars, and a focus on preventing crashes from occurring in the first place. We have more than a hundred safety innovations that do not exist in conventional cars today.

Zoox Autonomous Vehicle - Interior day.png
The vehicle features a four-seat, face-to-face symmetrical seating configuration that eliminates the steering wheel and bench seating seen in conventional car designs.
Zoox

We are also developing the AI, vehicle, and service all together. Integrating the software, sensor, and vehicle subsystems is a complex challenge that requires tight, cross-functional collaboration. It would be difficult to create this level of system integration across multiple companies with divergent commercial interests. Building a ground-up vehicle has allowed us to design and choose our own sensor suite to best solve self-driving. We’ve outfitted our Toyota Highlander fleet with this same sensor architecture as our ground-up vehicle so that we can gather large amounts of data and test in environments like San Francisco and Las Vegas while our in-house vehicle is still under development.

Our software stack includes mapping, localization, sensor calibration, perception, prediction, path planning, vehicle control, infrastructure, firmware, diagnostics/messaging/monitoring/logging, and simulation. All of this software is continuously improving, with additions of new features and iterative software updates that are put through rigorous offline validations and on-vehicle structured testing.

Our vehicles also use a variety of advanced sensors, including LIDAR, cameras, and radar, to see objects on all sides of the vehicle. And because of the geometrical configuration of these sensors, we can almost always see around and behind the objects nearest to us, which is particularly helpful in dense urban environments. Our software then uses a combination of machine learning and geometric reasoning to understand the sensor data, make sense of the scene unfolding around the vehicle, and effectively navigate the roads.

We’re excited to launch our first commercial driverless service, but we won’t do so until we’re ready to operate on public roads at safety levels that meaningfully surpass that of humans.
Jesse Levinson

For example, in a busy downtown intersection, our vehicle might be identifying a construction zone based on road cones and signs, while also detecting, tracking, and predicting the motion of hundreds of other agents (vehicles, pedestrians, bicyclists, etc.) around it. Once the perception system understands the environment and can predict how surrounding agents will move, the planner uses that information and context to adapt its driving behavior to the dynamic road conditions. The planner normally tries to maintain a certain lateral distance between itself and other vehicles, but it could decide to slightly reduce that distance in order to avoid a cone in the road ahead.

By combining both the hardware and software design, we are able to reimagine passenger safety. We are confident in our sensors’ abilities to detect activity in the environment around the vehicle, but that has to be validated in a wide range of scenarios. And our vehicle has performed extremely well in crash testing, which is still important, because no matter how sophisticated the AI is, we can’t guarantee that nothing will ever hit us. We’re excited to launch our first commercial driverless service, but we won’t do so until we’re ready to operate on public roads at safety levels that meaningfully surpass that of humans.

Research areas

Related content

US, WA, Seattle
Come be a part of a rapidly expanding $35 billion-dollar global business. At Amazon Business, a fast-growing startup passionate about building solutions, we set out every day to innovate and disrupt the status quo. We stand at the intersection of tech & retail in the B2B space developing innovative purchasing and procurement solutions to help businesses and organizations thrive. At Amazon Business, we strive to be the most recognized and preferred strategic partner for smart business buying. Bring your insight, imagination and a healthy disregard for the impossible. Join us in building and celebrating the value of Amazon Business to buyers and sellers of all sizes and industries. Unlock your career potential. The AB Sales Analytics, Data, Product and Tech (ADAPTech) team uses CRM, data, product, and science to improve Sales productivity and performance. It has four pillars: 1) SalesTech maintains Salesforce to enable Sales workflows, and supports >2K users in nine countries; 2) Product and Science builds tools embedded with bespoke Machine Learning (ML) and GenAI large language models to enable sales reps to prioritize top accounts, position the right Amazon Business (AB) product features, and take actions based on critical customer events; 3) Sales Data Management (SDM) and Sales Account Management (SAM) enrich customer profiles and business hierarchies while improving productivity through automation and integration of internal/external tools; and 4) Business Intelligence (BI) enables self-service reporting simplifying access to key insights through WBRs and dashboards. Sales teams leverage these products to identify which customers to target, what features to target them with, and when to target them, in order to capture their share of wallet. A successful Applied Scientist at Amazon demonstrates bias for action and operates in a startup environment, with outstanding leadership skills, and proven ability to build and manage medium-scale modeling projects, identify data requirements, build methodology and tools that are statistically grounded. We need great leaders to think big and design new solutions to solve complex problems using machine learning (ML) and Generative AI techniques to improve our customers’ experience when using AB. You have hands-on experience making the right decisions about technology, models and methodology choices. Key job responsibilities As an Applied Scientist, you will primarily leverage machine learning techniques and generative AI to outreach customers based on their life cycle stage, behavioral patterns, and purchase history. You may also perform text mining and insight analysis of real-time customer conversations and make the model learn and recommend the solutions. Your work will directly impact the trust customers place in Amazon Business. You will partner with product management and technical leadership to identify opportunities to innovate customer journey experiences. You will identify new areas of investment and work to align product roadmaps to deliver on these opportunities. As a science leader, you will not only develop unique scientific solutions, but also play a crucial role in shaping strategies. Additional responsibilities include: -Design, implement, test, deploy and maintain innovative data and machine learning solutions to further the customer experience. -Create experiments and prototype implementations of new learning algorithms and prediction techniques -Develop algorithms for new capabilities and trace decisions in the data and assess how proposed changes could potentially impact business metrics to cater needs of Amazon Business Sales -Build models that measure incremental value, predict growth, define and conduct experiments to optimize engagement of AB customers, and communicate insights and recommendations to product, sales, and finance partners. A day in the life In this role, you will be a technical expert with significant scope and impact. You will work with Technical Product Managers, Data Engineers, other Scientists, and Salesforce developers, to build new and enhance existing ML models to optimize customer experience. You will prototype and test new ideas, iterate quickly, and deploy models to production. Also, you will conduct in-depth data analysis and feature engineering to build robust ML models.
NL, Amsterdam
Are you a MS or PhD student interested in a 2025 Internship in the field of machine learning, deep learning, speech, robotics, computer vision, optimization, quantum computing, automated reasoning, or formal methods? If so, we want to hear from you! We are looking for students interested in using a variety of domain expertise to invent, design and implement state-of-the-art solutions for never-before-solved problems. You can find more information about the Amazon Science community as well as our interview process via the links below; https://www.amazon.science/ https://amazon.jobs/content/en/career-programs/university/science https://amazon.jobs/content/en/how-we-hire/university-roles/applied-science Key job responsibilities As an Applied Science Intern, you will own the design and development of end-to-end systems. You’ll have the opportunity to write technical white papers, create roadmaps and drive production level projects that will support Amazon Science. You will work closely with Amazon scientists, and other science interns to develop solutions and deploy them into production. You will have the opportunity to design new algorithms, models, or other technical solutions whilst experiencing Amazon’s customer focused culture. The ideal intern must have the ability to work with diverse groups of people and cross-functional teams to solve complex business problems. A day in the life At Amazon, you will grow into the high impact, visionary person you know you’re ready to be. Every day will be filled with developing new skills and achieving personal growth. How often can you say that your work changes the world? At Amazon, you’ll say it often. Join us and define tomorrow. Some more benefits of an Amazon Science internship include; • All of our internships offer a competitive stipend/salary • Interns are paired with an experienced manager and mentor(s) • Interns receive invitations to different events such as intern program initiatives or site events • Interns can build their professional and personal network with other Amazon Scientists • Interns can potentially publish work at top tier conferences each year About the team Applicants will be reviewed on a rolling basis and are assigned to teams aligned with their research interests and experience prior to interviews. Start dates are available throughout the year and durations can vary in length from 3-6 months for full time internships. This role may available across multiple locations in the EMEA region (Austria, Estonia, France, Germany, Ireland, Israel, Italy, Luxembourg, Netherlands, Poland, Romania, Spain, UAE, and UK). Please note these are not remote internships.
US, WA, Bellevue
Amazon Web Services (AWS) offers a broad set of global compute, storage, database, analytics, application, and deployment services that help organizations move faster, lower IT costs, and scale applications. These services are trusted by the largest enterprises and the hottest start-ups to power a wide variety of workloads including web and mobile applications, data processing and warehousing, storage, archive, and many others. We are looking for an applied scientist to help us define and build a new enterprise application. AWS Applications is building services in Supply Chain Management and is looking for a scientist to tackle complex science problems in Supply Chain including demand planning, supply planning and sustainability which will be used by our customers across a wide range of industries. We operate a fast growing business and our journey has only started. Our mission is to build the most efficient and optimal supply chain software on the planet, using our science and technology as our biggest advantage. We aim to leverage cutting edge technologies in optimization, operations research, and machine learning to grow our businesses. As an Applied Scientist, you’ll design, model, develop and implement state-of-the-art models and solutions used by users worldwide. As part of your role you will regularly interact with software engineering teams and business leadership. The focus of this role is to research, develop, and deploy models to improve state-of-the-art for time series. You will have the opportunity to work on our assistant solution allowing our users to ask data questions in natural language and get intelligent insights and exceptions. Key job responsibilities Lead and partner with the engineering to drive modeling and technical design for complex business problems. Develop accurate and scalable machine learning models to solve our hardest supply chain problems. Lead complex modeling analyses to aid management in making key business decisions and set product direction. A day in the life Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying.
US, WA, Seattle
We are building GenAI based shopping assistant for Amazon. We reimage 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, Seattle
Our team's mission is to improve Shopping experience for customers interacting with Amazon devices via voice. We work with Alexa and multiple other teams to research and develop advanced state-of-the-art speech technologies. Do you want to be part of the team developing the latest technology that impacts the customer experience of ground-breaking products? Then come join us and make history. Key job responsibilities We are looking for a passionate, talented, and inventive Research Scientist with a background in Machine Learning to help build industry-leading Speech and Language technology. As a Research Scientist at Amazon you will work with talented peers to develop novel algorithms and modelling techniques to drive the state of the art in speech synthesis. Position Responsibilities: * Participate in the design, development, evaluation, deployment and updating of data-driven models for Speech and Language applications. * Participate in research activities including the application and evaluation of Speech and Language techniques for novel applications. * Research and implement novel ML and statistical approaches to add value to the business.
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 Generative AI Innovation Center at AWS is a new strategic team that helps AWS customers implement Generative AI solutions and realize transformational business opportunities. This is a team of strategists, data scientists, engineers, and solution 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 make plans for launching solutions at scale. The GenAI Innovation Center team provides guidance on 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. We’re looking for Data Scientists capable of using GenAI and other techniques to design, evangelize, and implement state-of-the-art solutions for never-before-solved problems. Key job responsibilities - Collaborate with AI/ML scientists and architects to Research, design, develop, and evaluate cutting-edge generative AI algorithms 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, 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 ABOUT AWS: 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.
US, WA, Seattle
Are you motivated to explore research in ambiguous spaces? Are you interested in conducting research that will improve associate, employee and manager experiences at Amazon? Do you want to work on an interdisciplinary team of scientists that collaborate rather than compete? Join us at PXT Central Science! The People eXperience and Technology Central Science Team (PXTCS) uses 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. Key job responsibilities As an Applied Scientist for People Experience and Technology (PXT) Central Science, you will be working with our science and engineering teams, specifically on re-imagining Generative AI Applications and Generative AI Infrastructure for HR. Applying Generative AI to HR has unique challenges such as privacy, fairness, and seamlessly integrating Enterprise Knowledge and World Knowledge and knowing which to use when. In addition, the team works on some of Amazon’s most strategic technical investments in the people space and support Amazon’s efforts to be Earth’s Best Employer. In this role you will have a significant impact on 1.5 million Amazonians and the communities Amazon serves and ample scope to demonstrate scientific thought leadership and scientific impact in addition to business impact. You will also play a critical role in the organization's business planning, work closely with senior leaders to develop goals and resource requirements, influence our long-term technical and business strategy, and help hire and develop science and engineering talent. You will also provide support to business partners, helping them use the best scientific methods and science-driven tools to solve current and upcoming challenges and deliver efficiency gains in a changing marke About the team The AI/ML team in PXTCS is working on building Generative AI solutions to reimagine Corp employee and Ops associate experience. Examples of state-of-the-art solutions are Coaching for Amazon employees (available on AZA) and reinventing Employee Recruiting and Employee Listening.
IL, Tel Aviv
Come build the future of entertainment with us. Are you interested in helping shape the future of movies and television? Do you want to help define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows including Amazon Originals and exclusive licensed content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. We are looking for an Applied Scientist to embark on our journey to build a Prime Video Sports tech team in Israel from ground up. Our team will focus on developing products to allow for personalizing the customers’ experience and providing them real-time insights and revolutionary experiences using Computer Vision (CV) and Machine Learning (ML). You will get a chance to work on greenfield, cutting-edge and large-scale engineering and science projects, and a rare opportunity to be one of the founders of the Israel Prime Video Sports tech team in Israel. Key job responsibilities We are looking for an Applied Scientist with domain expertise in Computer Vision or Recommendation Systems to lead development of new algorithms and E2E solutions. You will be part of a team of applied scientists and software development engineers responsible for research, design, development and deployment of algorithms into production pipelines. As a technologist, you will also drive publications of original work in top-tier conferences in Computer Vision and Machine Learning. You will be expected to deal with ambiguity! We're looking for someone with outstanding analytical abilities and someone comfortable working with cross-functional teams and systems. You must be a self-starter and be able to learn on the go. About the team In September 2018 Prime Video launched its first full-scale live streaming experience to world-wide Prime customers with NFL Thursday Night Football. That was just the start. Now Amazon has exclusive broadcasting rights to major leagues like NFL Thursday Night Football, Tennis major like Roland-Garros and English Premium League to list few and are broadcasting live events across 30+ sports world-wide. Prime Video is expanding not just the breadth of live content that it offers, but the depth of the experience. This is a transformative opportunity, the chance to be at the vanguard of a program that will revolutionize Prime Video, and the live streaming experience of customers everywhere.
IL, Tel Aviv
Come build the future of entertainment with us. Are you interested in helping shape the future of movies and television? Do you want to help define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows including Amazon Originals and exclusive licensed content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. We are looking for a Senior Applied Scientist to embark on our journey to build a Prime Video Sports tech team in Israel from ground up. Our team will focus on developing products to allow for personalizing the customers’ experience and providing them real-time insights and revolutionary experiences using Computer Vision (CV) and Machine Learning (ML). You will get a chance to work on greenfield, cutting-edge and large-scale engineering and science projects, and a rare opportunity to be one of the founders of the Israel Prime Video Sports tech team in Israel. Key job responsibilities We are looking for a Senior Applied Scientist with domain expertise in Computer Vision or Recommendation Systems to lead development of new algorithms and E2E solutions. You will be part of a team of applied scientists and software development engineers responsible for research, design, development and deployment of algorithms into production pipelines. As a technologist, you will also drive publications of original work in top-tier conferences in Computer Vision and Machine Learning. You will be expected to deal with ambiguity! We're looking for someone with outstanding analytical abilities and someone comfortable working with cross-functional teams and systems. You must be a self-starter and be able to learn on the go. About the team In September 2018 Prime Video launched its first full-scale live streaming experience to world-wide Prime customers with NFL Thursday Night Football. That was just the start. Now Amazon has exclusive broadcasting rights to major leagues like NFL Thursday Night Football, Tennis major like Roland-Garros and English Premium League to list few and are broadcasting live events across 30+ sports world-wide. Prime Video is expanding not just the breadth of live content that it offers, but the depth of the experience. This is a transformative opportunity, the chance to be at the vanguard of a program that will revolutionize Prime Video, and the live streaming experience of customers everywhere.
IL, Tel Aviv
Come build the future of entertainment with us. Are you interested in helping shape the future of movies and television? Do you want to help define the next generation of how and what Amazon customers are watching? Prime Video is a premium streaming service that offers customers a vast collection of TV shows and movies - all with the ease of finding what they love to watch in one place. We offer customers thousands of popular movies and TV shows including Amazon Originals and exclusive licensed content to exciting live sports events. We also offer our members the opportunity to subscribe to add-on channels which they can cancel at anytime and to rent or buy new release movies and TV box sets on the Prime Video Store. Prime Video is a fast-paced, growth business - available in over 240 countries and territories worldwide. The team works in a dynamic environment where innovating on behalf of our customers is at the heart of everything we do. If this sounds exciting to you, please read on. We are looking for an Applied Scientist to embark on our journey to build a Prime Video Sports tech team in Israel from ground up. Our team will focus on developing products to allow for personalizing the customers’ experience and providing them real-time insights and revolutionary experiences using Computer Vision (CV) and Machine Learning (ML). You will get a chance to work on greenfield, cutting-edge and large-scale engineering and science projects, and a rare opportunity to be one of the founders of the Israel Prime Video Sports tech team in Israel. Key job responsibilities We are looking for an Applied Scientist with domain expertise in Computer Vision or Recommendation Systems to lead development of new algorithms and E2E solutions. You will be part of a team of applied scientists and software development engineers responsible for research, design, development and deployment of algorithms into production pipelines. As a technologist, you will also drive publications of original work in top-tier conferences in Computer Vision and Machine Learning. You will be expected to deal with ambiguity! We're looking for someone with outstanding analytical abilities and someone comfortable working with cross-functional teams and systems. You must be a self-starter and be able to learn on the go. About the team In September 2018 Prime Video launched its first full-scale live streaming experience to world-wide Prime customers with NFL Thursday Night Football. That was just the start. Now Amazon has exclusive broadcasting rights to major leagues like NFL Thursday Night Football, Tennis major like Roland-Garros and English Premium League to list few and are broadcasting live events across 30+ sports world-wide. Prime Video is expanding not just the breadth of live content that it offers, but the depth of the experience. This is a transformative opportunity, the chance to be at the vanguard of a program that will revolutionize Prime Video, and the live streaming experience of customers everywhere.