- 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
Base City:
Remote-Canada - Remote
Salary:
No Salary therefore no Star!
Rating:
Self-taught:
Position Type:
Full-time
Position Keywords:
Required:
Bachelor - Computer Science
Other Experience:
About the Job:
StackAdapt is a self-serve advertising platform that specializes in multi-channel solutions including native, display, video, connected TV, audio, in-game, and digital out-of-home ads. We empower hundreds of digitally-focused companies to deliver outcomes and exceptional campaign performance everyday. StackAdapt was founded with a vision to be more than an advertising platform, it’s a hub of innovation, imagination and creativity.
We're looking to add Data Engineers to our data team! This team works on solving complex problems for StackAdapt's digital advertising platform. You'll be working directly with our data scientists, data engineers, Engineering team, and CTO on building pipelines and ad optimization models. With databases that process millions of requests per second, there's no shortage of data and problems to tackle.
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 in various of our operating locations throughout the globe!
What they want you to do:
- 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