Applied Scientist
Posted 8 Oct 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 1+ yrs
- Management level
- Individual contributor
No salary listed for this role.
Field of studyComputer Science, Engineering, Mathematics, Physics, Statistics, Machine learning (or related)
Study levelBachelor's or higher
About Us
Twitch is the world’s biggest live streaming service, with global communities built around gaming, entertainment, music, sports, cooking, and more. It is where thousands of communities come together for whatever, every day.
We’re about community, inside and out. You’ll find coworkers who are eager to team up, collaborate, and smash (or elegantly solve) problems together. We’re on a quest to empower live communities, so if this sounds good to you, see what we’re up to on LinkedIn and X, and discover the projects we’re solving on our Blog. Be sure to explore our Interviewing Guide to learn how to ace our interview process.
About the Role
We are looking for an Applied Scientist to solve challenging and open-ended problems in the domain of Creators and content contextual signals. As an Applied Scientist on Twitch's GTM Science team, you will use applied machine learning to tackle ambiguous business problems across problem areas such as Ads, Brand Safety, Creator Sponsorship and Content Understanding. You will lead high profile projects where you will use a wide toolbox of ML tools to handle multiple types of data, including user behavior, metadata, and user generated content such as text and video. You will collaborate with a team of passionate scientists, engineers, and product stakeholders to develop these models, prototype and put them into production, where they can help Twitch's creators and viewers succeed and build communities.
You will report to our Senior Data Science Manager for GTM. This position is located in San Francisco, CA.
You Will:
- Build machine learning products to enrich Twitch’s content understanding and help creators and viewers build and discover their communities.
- Propose, design, and oversee execution for high-impact projects that work backwards from customer problems to develop the right solution for the job. Know when to use a classical ML model versus a state-of-the-art one.
- Collaborate with cross-functional engineering and product teams to prototype and deploy your models into flexible data pipelines and ML-based services.
- Bring innovation and help drive science culture on the team by staying up to date and experimenting with new techniques in LLM, computer vision, inference and model evaluation.