Netflix is revolutionizing the entertainment industry with world-class technology. They are both a content distributor and a producer for original and premium shows. They serve millions of subscribers worldwide in more than 190 countries around the world. They produce hundreds of new series, movies, documentaries, stand-up specials, and other categories of content each year. Because of their global footprint, they are able to elevate new types of creators, tell a diverse set of stories and inspire a global audience.
The Content Demand Modeling team, within Data Science and Engineering, plays a central role at the company and supports critical decisions such as predicting the value of content and understanding how key elements contribute to a title’s success. Arming their creative teams with data-driven insights improves both the efficiency and accuracy of decision-making and helps Netflix produce more hits and member joy at better economics.
The team consists of a mix of machine learning scientists (ML) and ML engineers. Their portfolio of projects ranges from production ML models that they support and innovate upon, to longer-term research projects with potential game-changing impact. Additionally, because their models inform content decisions, there is an emphasis on interpretability and user trust. Their shared mission is to scale decision-making, opportunity detection, and discovery for creative exploration.
As a machine learning engineer on the team, you will help lay the foundation that allows the team to accelerate and therefore more effectively scale its impact. Examples of specific problems you’ll help solve: detecting shifts in data used by ML models to identify issues in advance of deteriorating prediction quality, estimating the uncertainty of model outputs, automating prediction explanation for model diagnostics, designing a feature store that promotes sharing of data among different ML models, etc.
- Independently deliver effective solutions to problems.
- Own full-stack technology, from data to product and the feedback loop.
- Synthesize common patterns & build effective abstractions across different ML pipelines that accelerate the impact of ML-driven insights.
- Develop horizontal solutions to Increase the robustness of the team’s ML model portfolio.
- Partner with the Data Engineering & ML Infrastructure teams in a two-way exchange of best practices.
- Communicate results to a variety of audiences, technical and non-technical.
- Enact Netflix values in daily work and interactions.
- At least five years of experience in applied ML or ML systems/infrastructure.
- Effectively partner with applied ML scientists on the team and lay software foundations upon which models can be built.
- Experience building ML infrastructure, with an eye towards software engineering.
- “Product” orientation, with a high priority placed on the developer experience
- Excellent communication skills and an ability to translate business context and intuition into data-oriented hypotheses to drive impact.
- Experience with Spark or Hadoop and database schema design for ML pipelines.
- Strong coding experience (e.g. Python, SQL, open-source ML packages).
- Passion for and an appreciation of the entertainment industry is definitely a plus.
- Salary Offer 0 ~ $3000
- Experience Level Junior
- Total Years Experience 0-5
- Dropdown field Option 1