— Bachelor’s degree in Computer Science, Engineering, or a related field
— 4 + years of professional development experience building high-performance, large-scale applications/pipelines with solid experience in Python
— Solid foundation in computer science, with strong competencies in data structures, algorithms and software design
— Demonstrated self-motivation and willingness to dive into complicated DevOps challenges with a strong command of Linux and version control systems
— Experience working with cloud providers (Amazon Web Services, Google Cloud, etc.)
— Experience with distributed computation frameworks (Spark, Hive, Dask, Metaflow, etc.), job orchestration (Airflow, Luigi, etc.), and databases (MySQL, PostgreSQL, etc.).
— Strong verbal and written communication skills
— Familiarity with machine learning fundamentals such as training data and models
— Familiarity with container orchestration tools (Kubernetes, ECS, etc.) in a production setting
— Experience working with video—transport streams, video capture, video processing, transcoding, frame analysis, ffmpeg
— Experience with components of modern Machine Learning architectures—feature stores, model stores, evaluation stores, etc.
— Design, build, and refine scalable and secure ML infrastructure, using and extending existing solutions whenever possible
— Enable Machine Learning engineers to succeed in the end-to-end model development process by creating a cloud-native tools and processes that simplify working with labelled data, features, models, and relevant metrics
— Promote great engineering practices and help improve our processes and establish new ones
— Automate infrastructure maintenance and management, including cost monitoring
Our partner is on a mission to fundamentally change television viewing for everyone. They are doing this by leveraging our data to enable advertisers to engage and measure TV viewers across all their devices. They have an amazing story with a unique perspective formed by innovative technology.
Their team is in charge of building models for the next generation of AI-powered TV products. They are responsible for the end-to-end development of our models, including
— dataset collection using geographically-distributed television labs;
— model training in the cloud using serverless GPU clusters;
— model optimization for constrained computation on the edge;
— model testing using both virtual and real televisions; and
— development of the AI platform that makes the above possible.
As a member of their team you will be working at the intersection of engineering, science, and entertainment. As an ML Optimization Engineer, you will take the models we run in the cloud or other large reference models and optimize them to run on the constrained resources of an edge device, such as a TV.