Machine Learning Engineer

What you need to have:

• A proactive, autonomous, and results-oriented approach to problem-solving.
• Analytical mind with high attention to detail and the ability to manage multiple priorities.
• Strong foundation in statistical concepts and techniques: regression, properties of distributions, statistical significance tests, etc.
• Experience in machine learning and predictive modelling: neural networks, logistic regressions, gradient boosted trees and time series analysis.
• Good programming skills and familiarity with python and its libraries (pytorch, pandas, numpy, etc.).
• Competence in data analysis with SQL and pandas.
• Familiarity with Git.
• Excellent communication skills both verbal and written in English.

Extra points for :-)

• Master's degree in Machine Learning, AI, Statistics, Mathematics or Computer Science.
• Experience building high-performance algorithms in production environments.
• Competence with Apache Spark as well as Java or Scala programming languages.
• Familiarity with transformer architectures and LLMs.
• Experience in digital marketing platforms and user acquisition.
• Understanding of AWS services such as SageMaker, EMR, EC2, Athena and S3.

We compete in a highly advanced technology-driven industry, real time bidding programmatic advertising, where Machine Learning and cutting-edge cloud-based big data architectures are the biggest factor in achieving the company's growth.

Our mission is to continue to improve our ad-tech platform to help our clients achieve their programmatic advertising campaign goals, and we want to give our employees a job they'll love, where they will be challenged to improve results through real-life engineering and data analysis and where everyone's implication has an impact.

• Maximize our advertisers return on ad spend: Contribute to selecting the best ad and computing the optimal bid for each of the 4 million ad opportunities per second our platform processes.
• Modelling: Solve the challenges of the Real Time Bidding environment such as predicting the user response to an ad, the auction's closing price and much more! Address machine learning hurdles that may not be covered in the scientific literature, like extreme positive sample sparsity and labeling delay.
• Execute the project end-to-end: Research the state of the art, design, develop and deploy full-stack ML solutions at web scale. You own the logic and the implementation. You build it, you run it.
• Analyze the data and evaluate your improvements: Deep-dive into model evaluation and AB tests's statistical significance to ensure our growth is built rigorously on signal, not noise.
• Contribute with ideas and influence our decisions: Great execution is very valuable but we are also looking for great ideas to add to our product roadmap.

Are you ready to be part of the new unicorn? Keep reading!