Design and implement scalable data pipelines and ML algorithms to optimize StackAdapt's digital advertising platform. Collaborate with teams to solve complex problems.
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're looking to add a Machine Learning Engineer to our Data Science team! This team works on solving complex problems for StackAdapt's digital advertising platform. You'll be working directly with our Data Scientists, Machine Learning Engineers, Engineering teams, and our CTO/Co-Founder 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 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
StackAdapter's Enjoy:
- Highly competitive salary
- Retirement/ 401K/ Pension Savings globally
- Competitive Paid time off packages including birthday's off!
- Access to a comprehensive mental health care platform
- Health benefits from day one of employment
- Work from home reimbursements
- Optional global WeWork membership for those who want a change from their home office and hubs in London and Toronto
- Robust training and onboarding program
- Coverage and support of personal development initiatives (conferences, courses, books etc)
- Access to StackAdapt programmatic courses and certifications to support continuous learning
- An awesome parental leave program
- A friendly, welcoming, and supportive culture
- Our social and team events!
StackAdapt is a diverse and inclusive team of collaborative, hardworking individuals trying to make a dent in the universe. No matter who you are, where you are from, who you love, follow in faith, disability (or superpower) status, ethnicity, or the gender you identify with (if you’re comfortable, let us know your pronouns), you are welcome at StackAdapt. If you have any requests or requirements to support you throughout any part of the interview process, please let our Talent team know.
About StackAdapt
We've been recognized for our diverse and supportive workplace, high performing campaigns, award-winning customer service, and innovation. We've been awarded:
Ad Age Best Places to Work 2024
G2 Top Software and Top Marketing and Advertising Product for 2024
Campaign’s Best Places to Work 2023 for the UK
2024 Best Workplaces for Women and in Canada by Great Place to Work®
#1 DSP on G2 and leader in a number of categories including Cross-Channel Advertising
#LI-REMOTE
Top Skills
Algorithms
Data Pipelines
Distributed Systems
Machine Learning
Microservices
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