Staff Machine Learning Engineer, Platform

Position Summary

Position Summary

Samsung Ads is a fast-growing advanced advertising technology company that empowers advertisers to connect with audiences across Samsung devices through digital media. Leveraging the industry's most comprehensive first-party data, we are building the world's smartest advertising platform. As part of the global Samsung ecosystem, we tackle large-scale, complex projects alongside stakeholders and teams around the world.

We have built a world-class organization rooted in entrepreneurship and collaboration. At Samsung Ads, you'll discover just how fast you can grow, how much you can achieve, and how far you can go. We thrive on solving hard problems, breaking new ground, and enjoying the journey along the way.

Machine learning is at the heart of modern advertising, and Samsung Ads is no exception. We are actively exploring cutting-edge ML techniques to enhance existing systems, build new products, and unlock new revenue streams. As a Machine Learning Platform Engineer on the Platform Intelligence (PI) team, you will have access to Samsung's unique proprietary data to help develop and deploy large-scale machine learning products with real-world impact. You will work alongside experienced engineers and world-class researchers on exciting projects using state-of-the-art technologies. You will be welcomed by a culture of continuous learning, mentorship, and a creative work atmosphere. This is an excellent opportunity to accelerate your career by contributing to cutting-edge machine learning products within a rapidly growing team.

Location: Mountain View, CA

Responsibilities

Role and Responsibilities

• Develop and maintain machine learning platform components that support large-scale model training pipelines and batch prediction systems.
• Contribute to building a world-class ML platform tailored for Samsung's ML-based advertising business.
• Build and improve CI/CD pipelines, data workflows, and monitoring systems to enhance platform reliability and efficiency.
• Assist in researching and evaluating new machine learning platform technologies through prototypes and proof-of-concepts.
• Collaborate with internal ML teams (e.g., ML Serving and ML Engineering) to improve codebase quality and product health.
• Work with cross-functional partner teams to support the delivery of new ML features and solutions.
• Troubleshoot issues, optimize system performance, and contribute to engineering best practices.
• Learn quickly and adapt to a fast-paced working environment.
  
  
  

Experience Requirements

• 3-4 years of industry experience with a Bachelor's degree, or 2 years of industry experience with a Master's degree in Computer Science or related fields such as Statistics, Data Science, Technology, Engineering, or Mathematics.
• Solid programming skills in Python, with familiarity in SQL and databases.
• Foundational knowledge of machine learning concepts and hands-on experience with at least one ML framework (e.g., TensorFlow, PyTorch, or Spark ML).
• Familiarity with big data tools and concepts (e.g., Spark, Kafka, or similar technologies).
• Basic understanding of containerization (Docker) and orchestration (Kubernetes).
• Exposure to CI/CD pipelines, version control (Git), and software engineering principles.
• Understanding of data structures and algorithms.
• Good communication skills and ability to collaborate effectively in a team environment.
• Eagerness to learn new technologies and adapt to a fast-paced environment.
  
  
  

Skills And Qualifications

Preferred Experience Requirements

• Experience with cloud platforms, particularly Amazon Web Services (AWS).
• Familiarity with Infrastructure as Code (Terraform) or workflow orchestration tools (Airflow).
• Exposure to monitoring and alerting tools such as Prometheus or Grafana.
• Experience with Snowflake or similar data warehouse technologies.
• Interest in or exposure to the advertising industry and real-time bidding (RTB) ecosystem.
• Personal projects or contributions to open-source ML or data engineering projects.
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