Job Information
Amazon Principal Applied Scientist, Prime Video Global App Experience in Dallas, Texas
Description
At Prime Video we are inventing a customer-centric future that offers more choice and flexibility than traditional Cable, Satellite television and other streaming providers. Where you can watch video content such as Prime Originals (Fallout, The Boys, The Marvelous Mrs. Maisel), movies, TV shows, Live sports (Thursday Night Football, English Premier League), local programming and hundreds of add-on subscriptions such as HBO max and Paramount+, anytime anywhere. All on your terms instead of having to pay for an expensive one-size-fits-all bundled package or be locked into long term contracts.
The Global App Experience organization operates at a global scale and is a critical part of Prime Video’s flywheel. We own Prime Video's “last mile” customer experience including all the client apps (Android, iOS, Web, Living Room Smart TVs, set top boxes, Windows10, Mac OSX, gaming consoles) that enable customers in more than 200 countries and territories to find, discover and stream Prime Video content. We also own tier-1 services that act as aggregators and orchestrators between all of Prime Video's clients and Amazon's large backend service graph, providing authoritative APIs that reduce the complexity, cost and time to develop and launch features to our customers. Our client apps are the gateway to the Prime Video experience, enabling customers to quickly find something to watch at which point the app fades in the background and the customer is immersed in the storytelling.
If you are passionate about film, sport or TV and you’re looking for a role where you can maximize your impact as a world class Principal Applied Scientist then come help us achieve our mission of building the most customer-centric, immersive and visually-rich video streaming experience on any device.
Key job responsibilities
As a Principal Applied Scientist in the Prime Video Global App Experience organization you will have deep subject matter expertise in applied machine learning. You will work with multiple teams of scientists, engineers and product managers to translate business and functional requirements into concrete deliverables. The field of Artificial Intelligence and Machine learning is experiencing unprecedented innovation and while it is important to know how recent advancements, such as GenAI, can be applied to develop new approaches to improve customer, developer and business outcomes, you'll work backwards from the desired outcomes to determine which ML technique is best applicable (traditional ML, GenAI, etc). Problem spaces you will be working on include: improving the customer experience in the PV apps, reducing the cost/effort/time to detect defects in the customer experience, applying ML to all aspects of the software development process to allow us to develop and release features faster and improving availability, app fluidity and latency. You will also work with external academic partners to support our in-house talent with direct access to cutting edge research and mentoring.
We are open to hiring candidates to work out of one of the following locations:
Austin, TX, USA | Dallas, TX, USA | Seattle, WA, USA
Basic Qualifications
10+ years of tech industry or equivalent experience
Experience working effectively with science, data processing, and software engineering teams
Graduate degree in Computer science/Math or related field.
Experience in building complex, real-time systems involving AI, ML, NLP with successful delivery to customers.
Demonstrated track record of project delivery for large, cross-functional projects with evolving requirements. Ability to take a project from requirements gathering and design to actual product launch
Computer Science fundamentals in data structures, algorithm design and complexity analysis.
Ability to develop a machine learning strategy for non-traditional areas such as developer productivity, software quality assurance (testing), availability, app performance/fluidity and latency reduction.
Exceptional customer relationship skills including the ability to discover the true requirements underlying feature requests, recommend alternative technical and business approaches, and lead science efforts to meet aggressive timelines with optimal solutions.
Demonstrated track record of peer-reviewed scientific publications that advance state-of-the art for applied science.
Preferred Qualifications
PhD degree in Computer Science or related field.
Demonstrated ability to push the envelope in at least one machine learning domain (e.g. deep learning, NLP, reinforcement learning)
Expertise in large language models or demonstrated ability to develop this expertise quickly.
Experience in Computer Science fundamentals such as object-oriented design, algorithm design, data structures, problem solving, and complexity analysis.
Work with academic partners to support our in-house talent with direct access to cutting edge research and mentoring.
More than 15+ years of business/academic experience in building machine learning models.
Excellent written and verbal technical communication with an ability to present complex technical information in a clear and concise manner to a variety of audience.
Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from $159,100/year in our lowest geographic market up to $309,400/year in our highest geographic market. Pay is based on a number of factors including market location and may vary depending on job-related knowledge, skills, and experience. Amazon is a total compensation company. Dependent on the position offered, equity, sign-on payments, and other forms of compensation may be provided as part of a total compensation package, in addition to a full range of medical, financial, and/or other benefits. For more information, please visit https://www.aboutamazon.com/workplace/employee-benefits. This position will remain posted until filled. Applicants should apply via our internal or external career site.