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Principal Applied Research Machine Learning Engineer

1 month ago

Copenhagen, Copenhagen, Denmark Unity Full time
The Opportunity

At Unity, our mission is to empower creators to build immersive experiences that captivate audiences worldwide. As a key member of our Ads Applied Research team, you will play a crucial role in driving the overall Ads strategy for Unity's products and services.

What You'll Be Doing

As a machine learning and data science expert, you will guide and drive the application of machine learning solutions across the whole organization. Your role will be to:

  • Define and execute advanced machine learning models and algorithms to help translate complex business problems into actionable insights.
  • Stay at the forefront of the latest machine learning research to improve Unity's Ad platform Machine Learning capabilities.
  • Optimize, improve, and innovate ML practices to establish Unity as an industry leader.
  • Form and validate hypotheses on new aspects of modeling and potency of new datasets for ML.
  • Drive optimization hypotheses by analyzing the drivers for impactful machine learning and arguing the hypotheses and success for the next steps of development.
  • Making requirements and communicating data and machine learning capability needs to the product and engineering teams as the authority in Data for ML.
  • Driving the overall direction of the machine learning future for Ads to support strategic goals and vision.
What We're Looking For

We are looking for a candidate with experience in guiding machine learning development via revolutionary steps to keep Unity Ads at the forefront of ML performance. The ideal candidate will have:

  • A PhD in Computer Science, Mathematics, Statistics, or a related technical field or equivalent experience on interpreting and translating research to applications.
  • Experience successfully taking research and turning it into concrete product innovations with impact.
  • Expertise with modeling frameworks (Tensorflow, PyTroch) and understanding of model architectures and mechanisms.
  • Ability to explain machine learning and interpret the modeling efficacy for further improvements.
  • Capable of conducting analysis, statistics, and data literacy to deduct where the challenges and opportunities lie.