Machine Learning Engineer

Candidates should possess outstanding knowledge of machine learning frameworks and tools like PyTorch, Tensorflow, and Scikit-learn, with strong Python programming skills. A solid understanding of core machine learning and statistics, including Bayesian Modeling, Time Series Analysis, Reinforcement Learning, and Optimization is required. Experience in developing, deploying, and maintaining ML models in a production environment is necessary, along with the ability to thrive in a collaborative and diverse workplace. An ideal candidate will have approximately 10 years of experience in applied Machine Learning, delivering cloud-scale, data-driven products and services, and a PhD or Master's degree in Computer Science, Applied Math, Statistics, or a related field. Experience and domain expertise in Digital ad technologies are desirable, as is comfort with ambiguity, adaptability to evolving priorities, and the ability to influence collaborators. Strong problem-solving and execution abilities, along with innovation and a focus on delivering customer value, can compensate for some requirements.