About the team

ScaleMonk is building a brand new commercial product that helps mobile-first companies scale distribution while maintaining a target return. By collecting post-install data from its clients, ScaleMonk is able to optimize in real-time the ad spend that goes to different campaigns across hundreds of mobile advertising channels. 

We track 1.8 billion device profiles and handle 1,000,000 ad requests per second with a 1 ms response time. We train ML models to directly control millions of dollars of ad spend. 

We have a small, tightly focused engineering team. We do daily stand-ups often followed by design jams with people focused on a common part of the project. Source code control is via git.

About the Role

We are seeking an experienced Senior Machine Learning Engineer to build and deploy ML models at scale. 

What You'll Do

You will work on cutting edge high performance, high transaction and volume rate data with very low latency. Our system handles 1 million requests per second and responds in 1 ms. You will deploy cutting edge machine learning models into production. Our system trains artificial intelligence models on billions of examples and directly controls hundreds of millions of dollars of spend. You'll be challenged with solving extremely hard problems from scratch. You'll be motivated by the value you generate to the business, not just the accuracy of your models. You'll also be an excellent teammate and strong collaborator with all members of the team. 

What You'll Need

  • 3+ years of professional experience in Applied Machine Learning
  • Knowledge of machine learning that is both broad and deep
  • Great coding skills, familiarity with scripting languages (Python, R), distributed computing (Spark, Presto, or Hive) and version control (Git)
  • Excellent analytical, problem-solving and critical thinking skills
  • College degree in Computer Science, Statistics, Mathematics, a related field, or equivalent relevant experience

Nice to Have

  • 5+ years of professional experience working as a Applied Machine Learning
  • Experience in time series, hierarchical models, previous experience with product analytics
  • Experience developing systems in the ad tech ecosystem.

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