Machine Learning Engineer Intern - Winter 2027

StackAdapt is the leading technology company that empowers marketers to reach, engage, and convert audiences with precision. With 465 billion automated optimizations per second, the AI-powered StackAdapt Marketing Platform seamlessly connects brand and performance marketing to drive measurable results across the entire customer journey. The most forward-thinking marketers choose StackAdapt to orchestrate high-impact campaigns across programmatic advertising and marketing channels. We are searching for a talented Machine Learning Engineer Intern to join our team as we continue to expand our data science efforts. Our platform is connected to thousands of publishers and advertisers worldwide and as a result, we're dealing with millions of requests each second, making billions of decisions. We utilize the latest technologies to solve challenges in traffic, data storage, machine learning, and scalability. Want to learn more about our Data Science Team: https://alldus.com/ie/blog/podcasts/aiinaction-ned-dimitrov-stackadapt/ Learn more about our team culture here: https://www.stackadapt.com/careers/data-science Watch our talk at Amazon Tech Talks: https://www.youtube.com/watch?v=lRqu-a4gPuU StackAdapt is a Remote First company, and we are open to candidates located anywhere in Canada for this position. What you'll be doing: Design modular and scalable real time data pipelines to handle huge datasets Understand and implement custom ML algorithms in a low latency environment Work on microservice architectures that run training, inference, and monitoring on thousands of ML models concurrently What you'll bring to the table: Have the ability to take an ambiguously defined task, and break it down into actionable steps Have deep understanding of algorithm and software design, concurrency, and data structures Experience in implementing probabilistic or machine learning algorithms Interest in designing scalable distributed systems A high GPA from a well-respected Computer Science program Enjoy working in a friendly, collaborative environment with others The compensation range listed for this role reflects the expected base hourly pay for candidates located in the posting country based on a global rate. It is informed by market data and the approved budget for this position. StackAdapt maintains different compensation ranges for roles across other countries and regions, and final offers will be aligned to the candidate’s current location. We do not ask c andidates about current or prior compensation history, and we will not use such information, if volunteered, in setting an offer. This range represents base hourly compensation only. Factors Influencing Final Compensation: The final compensation offer will be determined by a variety of factors, which may include, but are not limited to: the candidate's specific experience, technical skills, knowledge, abilities, and relevant education, licensure, and…