Machine Learning Engineer

Integral Ad Science (IAS) is a leading global media measurement and optimization platform that delivers the industry’s most actionable data to drive superior results for the world’s largest advertisers, publishers, and media platforms. IAS’s software provides comprehensive and enriched data that ensures ads are seen by real people in safe and suitable environments, while improving return on ad spend for advertisers and yield for publishers. Our mission is to be the global benchmark for trust and transparency in digital media quality. For more information, visit integralads.com.

As a Machine Learning Engineer at IAS, you will be part of a team at the center of the company’s innovation strategy and a major contributor to the company's core products. You will work on challenging technical problems, using machine learning applied to multimodal content classification.

The types of challenges we solve have attracted people from industry and academia with diverse backgrounds, including machine learning, computer science, finance, physics, biology, neuroscience, and economics. We're passionate about maintaining an open and collaborative environment, where team members bring their own unique style of thinking and tools to the table.

The ideal candidate has a track record of delivering deep learning based models at scale, enjoys working with cross functional partners from Product and Engineering delivering solutions within strict timelines, and brings innovative solutions to complex problems with a desire to improve the status quo. 

What you’ll get to do:

• Collaborate on machine learning projects involving deep learning applications in image and video classification.
• Assist in building and deploying ML models using diverse and complex data sources.
• Work closely with senior ML data engineers, and product managers to design, test, and implement solutions.
• Learn to integrate ML models into production systems and monitor their performance.
• Communicate findings and insights clearly with technical and non-technical stakeholders.

What you’ll need:

• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Statistics, or a related technical field.
• Foundational knowledge of machine learning and deep learning concepts.
• Experience with Python and with deep learning frameworks (Pytorch, Tensorflow)
• Some exposure to data manipulation tools (Pandas, NumPy, Spark)
• A strong desire to learn, improve, and take on new challenges.
• Good communication skills and ability to work well in a team-oriented environment.

What puts you over the top:

• Practical experience with image/video classification, object detection, speech recognition, machine translation, and similar problems. 
• Publication record in deep learning / neural networks or adjacent fields 
• Experience working with distributed computing tools applied to machine learning (Ray, horovod), or to data processing  and cloud platforms (AWS, GCP)