DoubleVerify is a big data and analytics company. We track and analyze tens of billions of ads every day for the biggest brands in the world like Apple, Nike, AT&T, Disney, Vodafone, and most of the Fortune 500 companies. If you ever saw an Ad online via Web, Mobile, or CTV device then there are big chances that it was analyzed and tracked by us.
We operate at a massive scale, our backend handles over 100B+ events per day, we analyze and process those events in real-time while making decisions on the environment where the ad is running and all the user interactions during the Ad display lifecycle. We verify that all Ads are Fraud Free, Brand Safe, in the right Geo and highly likely to be viewed and engaged, all that in less than a fraction of a second.
We are global, we have R&D centers in Tel Aviv, New York, Finland, Belgium and San Diego, we work in a fast-paced environment and have a lot of challenges to solve. If you like to solve big data challenges and want to help us build a better industry then your place is with us.
What will you do
You will join a team of experienced engineers and help them in developing our innovative measurements products
Build backend infrastructure (data processing jobs, micro-services), create automated workflows to process large datasets for machine learning purposes
Develop ML/AI models through the entire development life-cycle in NLP and Vision domains.
Design and develop MLOps infrastructure to support our ML/AI models at scale, including CI/CD, automation, evaluation, and monitoring
Evaluate various open-source tools to accelerate and orchestrate processes
Create ML/AI model A/B testing and impact analysis tools
Who you are
At least 5+ years experience in backend/data engineering writing code that deployed and used in production
2+ years of experience as a Machine Learning Engineer
Proficient in Python and its ecosystem, knowledge of Scala/Kotlin/Java - is a plus
Familiarity with various ML algorithms and frameworks (PyTorch, Tensorflow, HuggingFace, Sklearn, etc..)
Experience working with various Big data technologies and tools (DataBricks, Snowflake, BigQuery, Kafka, Spark, Airflow, Argo) at scale