Machine Learning Model Engineer – Samsung Ads
Position Summary
Samsung Ads is an advanced advertising technology company in rapid growth that focuses on enabling advertisers to connect audiences from Samsung devices as they are exposed to digital media, using the industry’s most comprehensive data to build the world’s smartest advertising platform. Being part of an international company such as Samsung and doing business around the world means that we get to work on big, complex projects with stakeholders and teams located around the globe.
We are proud to have built a world-class organization grounded in an entrepreneurial and collaborative spirit. Working at Samsung Ads offers one of the best environments in the industry to learn just how fast you can grow, how much you can achieve, and how good you can be. We thrive on problem-solving, breaking new ground, and enjoying every part of the journey.
Machine learning lies at the core of the advertising industry, and this is no exception to Samsung Ads. At Samsung Ads, we are actively exploring the latest machine learning techniques to improve our existing systems and products and create new revenue streams. As a machine learning model engineer of the Samsung Ads Platform Intelligence (PI) team, you will have access to unique Samsung proprietary data to develop and deploy a wide spectrum of large-scale machine learning products with real-world impact. You will work closely with and be supported by a talented engineering team and top-notch researchers to work on exciting machine learning projects and state-of-the-art technologies. You will be welcomed by a unique learning culture and creative work atmosphere. This is an exciting and unique opportunity to get deeply involved in envisioning, designing and implementing cutting-edge machine learning products with a fast growing team.
Responsibilities
Experience Requirements:
Preferred Experience Requirements:
CALIFORNIA ONLY
Compensation for this role is expected to be between $240,000 and $280,000. Actual pay will be determined considering factors such as relevant skills and experience, and comparison to other employees in the role.
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