Integral Ad Science (IAS) is a global technology and data company that builds verification, optimization, and analytics solutions for the advertising industry, and we’re looking for a Machine Learning Operations Engineer to join our Data Science team. If you are excited by technology that has the power to handle hundreds of thousands of transactions per second; collect tens of billions of events each day; and evaluate thousands of data-points in real-time all while responding in just a few milliseconds, then IAS is the place for you!
As a Machine Learning Operations Engineer, you will be part of a team that is at the center of innovation and data quality, and a major contributor to the company’s core products. You will design and implement data pipelines, build and run tools into large scale production systems in the brand safety domain for advertising inventory across open web, social networks, video/CTV, and mobile apps. As part of the Trust & Safety team within the data science organization, you will be responsible for multimedia data pipeline building & maintenance, tool & services design for data inspection, and data visualization of model quality.

You should apply if you have most of of this experience:

• Master’s Degree in Information, Data or Computer Science/Engineering
• 1-3 years of experience in building custom integration between cloud based systems, data pipeline design and/or API/SDK interaction with 3rd party companies
• Experience in Python, SQL, Cloud Environments (AWS, Snowflakes), Databricks, Github
• Good to have: experience working with annotation crowd/tools, ability to understand tools used by data scientists, knowledge of Big Data frameworks, experience in API interaction, Agile, and CI/CD
• Good communication skills, team-oriented
• Fluent in Englis

• Work with the team to design/maintain data pipelines and engineering infrastructure to support our annotation systems at scale
• Develop and deploy scalable tools and services to handle data for annotated data inspection, biases, and error patterns investigation
• Build data visualization dashboards to provide results in a digestible way
• Apply software engineering rigor and best practices in database handling, CI/CD, automation, and code cleanliness
• Collaborate with taxonomists, knowledge engineers, linguistics, machine learning researchers, data scientists, data engineers
• Work across multiple time zones, within your time zone working hours

You should apply if you have most of of this experience:

• Master’s Degree in Information, Data or Computer Science/Engineering
• 1-3 years of experience in building custom integration between cloud based systems, data pipeline design and/or API/SDK interaction with 3rd party companies
• Experience in Python, SQL, Cloud Environments (AWS, Snowflakes), Databricks, Github
• Good to have: experience working with annotation crowd/tools, ability to understand tools used by data scientists, knowledge of Big Data frameworks, experience in API interaction, Agile, and CI/CD
• Good communication skills, team-oriented
• Fluent in English