Who are we?
RZR is an AI-driven company specializing in mobile advertising solutions designed to fuel revenue growth. We leverage AI to discover audiences in a privacy-first environment through trillions of contextual bidding signals and proprietary behavioral models. Our audience engagement platform includes creative strategy and execution. We handle 5 million mobile ad requests per second from over 10 billion devices, driving performance for both publishers and brands. We are headquartered in San Francisco, CA, with a global presence across the United States, EMEA, and APAC.
Role Overview
We are seeking a motivated and detail-oriented Machine Learning Engineer to join our team. As an ML Engineer, you will be involved in designing and implementing machine learning models and data pipelines to enhance our programmatic demand-side platform (DSP). You will work closely with Senior MLE and other team members to drive impactful machine learning projects and contribute to innovative solutions.
Key Responsibilities
- Support the development of machine learning models to address challenges in programmatic advertising, such as predicting user responses, forecasting bid landscapes, and detecting fraud.
- Collaborate with senior data scientists and cross-functional teams (product, engineering, and analytics) to integrate models into production workflows.
- Analyze the impact of integrating new data sources and features into our models.
- Build and maintain data pipelines to process and prepare large datasets for model training and evaluation.
- Contribute ideas and assist in testing new tools, methodologies, and technologies to improve our machine learning capabilities.
- Document experiments, assumptions, and outcomes; maintain reproducibility
Required Skills / Experience
- Bachelor’s degree in Mathematics, Physics, Computer Science, or a related technical field.
- 1-3 years of professional experience in machine learning, statistical analysis, and data analysis.
- Experience with machine learning techniques such as regression, classification, and clustering.
- Proficiency in Python and SQL and familiarity with big data tools (e.g., Spark) and ML libraries (e.g., TensorFlow, PyTorch, Scikit-Learn).
- Strong grasp of probability, statistics, and data analysis principles.
- Ability to work effectively in a team environment, with good communication skills to explain complex concepts to diverse stakeholders.
Nice-to-Have
- Familiarity with system programming languages including C++ and Rust is a plus.
- Exposure to online inference systems, gRPC/REST model endpoints, or streaming features (Kafka/Flink)
- Ad-tech familiarity: auction dynamics, pacing, fraud signals, creative personalization.