At Netflix, we entertain and bring joy to people across the world through amazing stories. We think of the Netflix service less as a monolithic product and more as hundreds of millions of different products by serving uniquely personalized experiences to each of our 200+ Million members. Machine learning is at the central driver of innovation across many aspects of Netflix’s business, from personalization recommendation algorithms at scale, content and script insights, to media asset processing.
The Machine Learning Platform organization’s mission is to maximize the business impact of machine learning practitioners at Netflix. We do this through building an ML Platform that helps scale and enable all stages of the ML lifecycle, including ad-hoc exploration and experimentation, preparing training data, model development, and robust production deployment. The Model Development team within the Machine Learning Platform organization is focused on enabling innovation in offline experimentation for the large diversity of ML use cases within Netflix.
In this role you will help define and execute the strategy and vision for enabling standard and novel experimentation practices at Netflix, including model training, ensembling and pipeline componentization, hyperparameter optimization, feature selection and engineering, model reuse, model evaluation/validation, performance profiling and optimization. You will build systems, infrastructure and libraries to provide a robust and transparent platform for experimentation at scale, aiming to consistently deliver “member joy” to our platform customers. You will work within the larger Machine Learning Platform org and the applied ML research community at Netflix to set the forward looking direction for nascent platform areas of investment like AutoML and ML Performance.
To be successful, you will need a deep understanding of software engineering and ML, with a particular eye for designing ergonomic, fluent and flexible systems for ML practitioners. You will need the ability to learn quickly, work collaboratively with other engineers and scientists, and to translate research needs and opportunities into scalable, easy to use solutions. A thoughtful and practical approach to building infrastructure, the ability to empathize and understand the needs of our customers, and rely on your applied machine learning experience to provide polished experiences to end users.
Work on this team spans critical functional integrations with the compute and data stack, in addition to high level machine learning tooling and libraries used to directly accelerate and automate common ML workflows for the diversity of ML use cases at Netflix.
You will have an opportunity to accelerate innovation in one of the premier machine learning powered companies in the world today, that is redefining how video content is consumed globally.
- Strong bias towards action, great curiosity, and excellent communication skills
- Experience designing end-user software with good API design sensibilities
- Exposure to working with high-scale distributed systems
- Experience in successfully applying machine learning to real-world domains
- BS/MS in Computer Science, Electrical Engineering or a related field
- 5+ years of professional experience
- Experience developing ML experimentation platforms, libraries or tools
- Experience with model tuning strategies: feature engineering, hyperparameter optimization, feature selection, deep learning optimization (e.g. neural architecture search)
- Experience with open source ML libraries such as Tensorflow, PyTorch, XGBoost, SkLearn
- Experience or exposure to profiling and optimizing deep learning workloads
- Experience or exposure to AutoML or black box optimization areas.
- Experience working with production data pipelines
- Experience using Scala, Python, Spark, AWS services
- Exposure to the Recommender Systems domain
Netflix is an equal opportunity employer and strives to build diverse teams
from all walks of life. We offer a unique culture
of freedom and responsibility with a clear long-term view
. We recommend reading through these to understand what working at Netflix is like.