Sr. Applied Scientist, Prime Video - Title Lifecycle Presentation

AI in this role

tensorflowscikit-learn

ai-agentscomputer-vision

Prime Video is a first-stop entertainment destination offering customers a vast collection of premium programming in one app available across thousands of devices. Prime members can customize their viewing experience and find their favorite movies, series, documentaries, and live sports – including Amazon MGM Studios-produced series and movies; licensed fan favorites; and programming from Prime Video add-on subscriptions such as Apple TV+, Max, Crunchyroll and MGM+. All customers, regardless of whether they have a Prime membership or not, can rent or buy titles via the Prime Video Store, and can enjoy even more content for free with ads.

Are you interested in shaping the future of entertainment? Prime Video's technology teams are creating best-in-class digital video experience.

As a Prime Video technologist, you’ll have end-to-end ownership of the product, user experience, design, and technology required to deliver state-of-the-art experiences for our customers. You’ll get to work on projects that are fast-paced, challenging, and varied. You’ll also be able to experiment with new possibilities, take risks, and collaborate with remarkable people.

We’ll look for you to bring your diverse perspectives, ideas, and skill-sets to make Prime Video even better for our customers. With global opportunities for talented technologists, you can decide where a career Prime Video Tech takes you!

The Prime Video Title Lifecycle Presentation team sits at the intersection of science, experimentation, and customer experience. We leverage data signals and rigorous testing to present the most engaging information about our content to customers at precisely the right moment. Our mission is to ensure every customer interaction with Prime Video content is informed, relevant, and compelling in order to drive discovery and engagement across our vast catalog.

We're seeking a Sr. Applied Scientist who excels at building sophisticated machine learning systems for content presentation and discovery. The ideal candidate brings deep expertise in:

- Multi-modal embeddings for rich metadata representation, enabling nuanced understanding of content attributes and customer preferences
- Contextualized ranking systems that adapt to customer intent, viewing context, and real-time signals
- Reinforcement learning frameworks that create continuous improvement loops, allowing our systems to learn and optimize from customer interactions over time
- General modeling techniques with strong fundamentals in machine learning and statistical methods
- Recommender systems experience, with proven ability to build and scale personalization solutions

You'll work with technology to solve complex problems in content discovery, leveraging large-scale data to create experiences that delight millions of Prime Video customers worldwide.

Key job responsibilities
- Lead Cross-Functional Science Initiatives: Drive a diverse portfolio of applied science projects spanning recommender systems, generative AI agent development and evaluation across multiple modalities, and computer vision applications. Demonstrate both breadth of understanding across technical domains and sufficient depth in each area to effectively lead multiple concurrent initiatives to successful outcomes.

- Bridge Science and Engineering for Production-Scale Deployment: Partner with engineering teams to productionize machine learning models at Prime Video scale. Develop production-ready science code that meets engineering standards for performance, reliability, and maintainability, ensuring seamless transition from research to deployment.

- Mentor and Develop Technical Talent: Provide technical mentorship and guidance to junior scientists and engineers on applied science methodologies, best practices, and professional development. Foster a culture of scientific rigor and continuous learning within the team.

Basic qualifications

- 3+ years of building machine learning models for business application experience
- PhD, or Master's degree and 6+ years of applied research experience
- Experience programming in Java, C++, Python or related language
- Experience with neural deep learning methods and machine learning

Preferred qualifications

- Experience with modeling tools such as R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy etc.
- Experience with large scale distributed systems such as Hadoop, Spark etc.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, SEATTLE - 167,100.00 - 226,100.00 USD annually

How we rate this

Sr. Applied Scientist, Prime Video - Title Lifecycle Presentation at Amazon rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

AI AgentsComputer VisionTensorFlowscikit-learn

Questions you could be asked

How do you decide when an AI agent can act on its own versus asking for approval first?
Walk me through a computer vision problem you solved, from raw data to a deployed model.
What are the limits of TensorFlow that you've run into, and how did you work around them?
What's a project where you used scikit-learn hands-on?
How would you decide a model or AI system is ready to ship?

Adapt your resume

List these exact terms on your resume: AI Agents, Computer Vision, TensorFlow, and scikit-learn. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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