"Alerted.org

Applied Scientist

@ Twitch Seattle, WA

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 recommendations, search, ranking and information retrieval. As an Applied Scientist on Twitch's Community team, you will use ML to help viewers find streamers and communities they’ll love. You will collaborate with a team of passionate scientists and engineers to develop these models and put them into production, where they can help Twitch's creators and viewers succeed and build communities.

You will report to the Applied Science Manager on the Community Discovery Team. You can work in San Francisco, CA or Seattle, WA.

You Will:

+ Develop ML algorithms for recommendations, ranking and search problems that can improve discovery on Twitch.

+ Collaborate with senior scientists and engineers to deploy ML code in production.

+ Collaborate with our Product and Engineering teams to work backwards from customer discovery problems, in order to determine the ML solution (algorithm and pipeline) that will have the biggest impact on our user base in the real world.

+ Participate in the scientific community at Twitch, Amazon, and the broader ML and risk community.

You Have:

+ BS or MS or PhD in Computer Science, Machine Learning, Data Science, Statistics, Mathematics, with relevant industry experience. We welcome applicants with non-traditional backgrounds. We welcome applicants with non-traditional backgrounds.

+ Hands-on experience with predictive modeling and analysis to solve real-world problems.

+ Proficiency with Python and Deep Learning Frameworks such as PyTorch or Tensorflow, basic proficiency with SQL.

Bonus Points

+ MS/PhD in relevant fields or a Degree with a specialization in Machine Learning.

+ Experience shipping your models to production.

+ 1+ years of experience working in recommendations or search.

+ Papers in top ML conferences of journals such as Neurips, Recsys, ICLR, ICML etc.

+ Familiarity with Twitch, its business, and its community.

Perks

+ Medical, Dental, Vision & Disability Insurance

+ 401(k)

+ Maternity & Parental Leave

+ Flexible PTO

+ Amazon Employee Discount

_Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records._

Job ID: TW8947

_Our compensation reflects the cost of labor across several US geographic markets. The base pay for this position ranges from our lowest geographic market up to 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. This position will remain open until filled. For more information, please visit_ _https://www.twitch.tv/jobs/en/#learn-more_ _._ _Applicants_ _should apply_ _via_ _our internal or external career site._

US Pay Per Year

$129,400—$212,800 USD

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

_Twitch values your privacy. Please consult our_ _Candidate Privacy Notice_ _, for information about how we collect, use, and disclose personal information of our candidates._