Applied Scientist, Prime Video Sports

Amazon is hiring an Applied Scientist, Prime Video Sports in Tel Aviv, Israel. Level rates it ; you can apply on Amazon.

Level's read on this job

Level rates this applied scientist role Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include computer vision.
Level lists 20,000+ other open roles at Amazon.
The role was posted in the last few days.

AI in this role

computer-vision

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 join our journey to build a Prime Video Sports tech team in Israel from the ground up. Our team focuses on developing products that personalize the customer experience and provide real-time insights using Computer Vision (CV) and Machine Learning (ML). You will get the chance to work on greenfield, large-scale science and engineering projects.

Key job responsibilities
We are looking for an Applied Scientist to lead the development of new Computer Vision algorithms and end-to-end solutions. You will research, design, develop, and deploy algorithms into production pipelines as part of a team of applied scientists and software development engineers. You will also drive publications of original work as patents and, optionally, in top-tier Computer Vision and Machine Learning conferences. This role involves collaborating across cross-functional teams and systems, and navigating evolving technical challenges in a greenfield environment.

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. Today, Amazon holds exclusive broadcasting rights to major leagues including NFL Thursday Night Football, Tennis major Roland-Garros, and the English Premium League, broadcasting live events across 30+ sports world-wide. Prime Video is expanding not just the breadth of live content it offers, but the depth of the experience. This is a unique opportunity to be at the forefront of a program that will transform how customers enjoy live sports streaming.

Basic qualifications

- Experience developing and implementing deep learning algorithms, particularly with respect to computer vision algorithms
- Master's degree or above in computer science, computer engineering, or related field
- Experience building machine learning models or developing algorithms for business application
- Experience in any of the following areas: algorithms and data structures, parsing, numerical optimization, data mining, parallel and distributed computing, high-performance computing
- Experience programming in Java, C++, Python or related language

Preferred qualifications

- Experience with machine learning techniques for fast and accurate extraction of visual features of humans (i.e. shape, pose, segmentation) from photographs
- Experience with generative deep learning models applicable to the creation of synthetic humans like CNNs, GANs, VAEs and NF
- Extensive evidence of deep learning, computer vision, human robotic interaction, algorithms implementation experience

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.

How we rate this

Applied Scientist, Prime Video Sports at Amazon rates 91 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. Read the description and count the AI tools, models and workflows it names.

Key facts

Applied Scientist, Prime Video Sports is an open job at Amazon.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in IL, Tel Aviv.
The job was posted on November 4, 2026.
Applications are handled by Amazon. Apply on Amazon

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Computer vision

Also common in applied scientist jobs at this level

LLM Generative AI NLP Reinforcement learning TensorFlow AWS

Pay

Typical applied scientist job: $172k

This posting doesn't list pay. Use the typical applied scientist figure when pay comes up. See applied scientist jobs

Questions you could be asked

Walk me through a computer vision problem you solved, from raw data to a deployed model.
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: Computer vision. 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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