Principal Applied Scientist, Prime Video Core Playback
1 month ago
The Core Playback organization operates at a global scale and is a critical part of Prime Video’s flywheel. We own Prime Video's core streaming technology and playback experience across all devices including Android phones and tablets, iOS devices, Web, Living Room Smart TVs, set top boxes, Windows10, Mac OSX, gaming consoles in more than 200 countries and territories. The technology landscape in streaming playback is rapidly changing. The Principal Scientist role in Core Playback reports to the Director of the organization with the intention of steering our science strategy across a wide range of areas.
Key job responsibilities
As a Principal Applied Scientist in the Prime Video Core Playback 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 streaming experience, video streaming and delivery heuristics in the PV apps, generating actionable insights from large scale of playback telemetry data, reducing the cost/effort/time to detect defects in customer's playback Quality of Experience (QoE) , applying ML to all aspects of the software development process to allow us to develop and release features faster and improving streaming playback quality and reliability. You will also work with external academic partners to support our in-house talent with direct access to cutting edge research and mentoring.
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 the 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.
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