Job Description

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 industrys most comprehensive data to build the worlds 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

• Lead and deliver production-grade machine learning products from end to end to make Samsung Ads a key player in the mobile ads market.
• Design, develop and deploy state-of-the-art and scalable machine learning models to achieve different optimization goals, such as ads click (pCTR), app-install optimization, ROAS optimization, retention, etc.
• Research the latest machine learning technologies with industry trends, create prototypes of new ML solutions quickly, and deploy the solution into production.
• Analyze complex problems with massive advertising data, identify gaps, and propose and execute technical proposals.
• Closely work with different internal ML teams (e.g., ML platform, ML serving, and MLOps teams) to improve our codebase and product health.
• Closely work with cross-functional partner teams in global settings to deliver new ML features and solutions and achieve business objectives.
• Mentor junior engineers and provide technical guidance.
• Learn quickly and adapt to a fast-paced working environment.

Experience Requirements:

• Masters or PhD degree in Computer Science or related fields.
• 4+ years of industry experience with a Masters degree or 2+ years of industry experience with a PhD degree.
• Solid theoretical background in machine learning and/or data mining.
• Proficiency in mainstream ML libraries (e.g., TensorFlow, PyTorch, Spark ML, etc.).
• Hands-on experience with production-grade machine learning solutions.
• Experience with mainstream big data tools (e.g., MapReduce, Spark, Flink, Kafka, etc.).
• Extensive programming experience in Python, Go or other OOP languages.
• Familiarity with data structures, algorithms and software engineering principles.
• Proficiency in SQL and databases.
• Strong communication and interpersonal skills to drive cross-functional partnerships.

Preferred Experience Requirements:

• Publications in top relevant venues (e.g., TPAMI, NeurIPS, ICML, ICLR, KDD, SIGIR, AAAI, IJCAI, etc.).
• Basic knowledge about Amazon Web Services (AWS).
• Experience with the advertising industry and real-time bidding (RTB) ecosystem.

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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