Samsung Ads is an advanced advertising ecosystem, spanning hundreds of millions of smart devices across TV, mobile, desktop, and beyond. The project we are recruiting for is focused on enabling brands to connect with Samsung TV audiences building the world’s smartest advertising platform. We use machine learning algorithms for advertising campaigns to enhance targeting, personalization, and optimization. The goal is to deliver the right message to the right audience at the right time, resulting in higher engagement rates and conversion rates.
Audience building is a crucial aspect of effective marketing, especially in todays digital landscape where targeting specific groups of people is essential for success.
During project on-boarding process you will understand our products and services to easily identify the ideal customer persona, considering factors such as demographics, psychographics, and purchasing power.
Being part of an international company such as Samsung you will get to work on the most challenging projects with stakeholders and teams located around the globe.
You will deeply dive into Samsung Advertising Galaxy working with such exciting domains like bidding, pacing and performance-based advertising, as well as recommendations and churn prediction/prevention.
As a machine learning engineer of the Samsung Ads team, you will have access to unique Samsung proprietary data to address existing product challenges and build end-to-end solutions with real-world impact. You will also work with talented engineers and top-notch machine learning researchers on exciting projects and state-of-the-art technologies.
In conclusion, you will play a crucial role in designing, building, and optimizing ML models and platform that supports the end-to-end ML lifecycle, from data ingestion and preparation to model training, deployment, and monitoring. By contributing to the development of high-quality ML models and platform, you will help to democratize access to ML capabilities, accelerate the pace of ML adoption, and foster collaboration among stakeholders.
Technologies in use
- Python (pandas, scikit-learn, matplotlib, etc.)
- TensorFlow
- PyTorch
- AWS (SageMaker)
- PySpark / Snowpark
- Snowflake (SQL)
- Github Actions
- Airflow
- Grafana
- Pytest, unittest
responsibilities :
Develop, test, deploy, and maintain data, scalable low-latency machine learning products and pipelines supporting ML products considering factors such as the nature of the data, the complexity of the problem, and the available computational resources.
Validate the models performance on unseen data, ensuring that it generalizes well and does not overfit the training data. Conduct rigorous testing to identify and address potential issues, such as bias or fairness concerns.
Design and develop the next generation machine learning platform to support thousands of model training pipelines concurrently and thousands of billions of daily batch predictions.
Research the latest machine learning platform technologies pushing the boundaries of what is currently possible with ML and keep up-to-date with industry trends and developments.
Experiment with new ML platforms tailored to our environment and create quick prototypes / proof-of-concepts.
Streamline model deployment, unit testing, integration testing, and stress testing and ensure engineering quality.
Support automation of the ML pipeline using CI/CD principles, promoting consistency, reproducibility, and agility.
Work with Data Scientists to introduce new ML platform features, help streamline the model development process, and reduce the lead time for model production.
Closely work with different internal ML teams (e.g., Data Scientists and MLOps teams) to improve codebase and product health.
Depending of your skills and experience you will have a chance to technically lead people
requirements-expected :
Degree in Computer Science or related field (Data Science, Big Data, Mathematics, Physics, etc.).
At least 2 years of proven industry experience in machine learning projects.
Strong programming skills in Python and data-science libraries (pandas, scikit-learn).
Working knowledge of SQL and relational databases, ideally Snowflake.
Expert knowledge of data analysis, statistics, data mining, machine learning or Big Data processing area.
Experience with mainstream ML libraries (TensorFlow/PyTorch, Scikit-Learn, PySpark etc.).
Familiarity with data structures, CI/CD pipelines, software engineering principles and robust code testing.
Collaborating in a team using code versioning tools like Git.
Ability to summarize and present the finding results to business/product stakeholders.
Openness to learn new technologies and adapt to technological stack.
offered :
Friendly atmosphere focused on teamwork
Wide range of trainings and a huge support in developing algorithmic skills
Opportunity to work in multiple projects
Working with the latest technologies on the market
Monthly integration budget
Possibility to attend local and foreign conferences
Flexible working hours
PC workstation/Laptop + 2 external monitors
Office in Warsaw Spire near Metro station
Working in a hybrid model: 3 days from the office per week
Attractive relocation package
benefits :
sharing the costs of sports activities
private medical care
sharing the costs of foreign language classes
life insurance
corporate products and services at discounted prices
integration events
dental care
no dress code
leisure zone
pre-paid cards
baby layette
charity initiatives
unlimited free access to Copernicus Science Center
