Senior Machine Learning Engineer

About the Company

RZR Global 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.

Responsibilities

• Lead development of complex ML models that directly impact business KPIs such as campaign selection and conversion prediction
• Design and own scalable evaluation frameworks including offline metrics, backtesting, and online A/B analysis
• Own end-to-end deployment of new models, tools, and architectural improvements
• Strengthen real-time monitoring systems to detect performance degradation
• Improve inference latency and address scaling constraints in training infrastructure
• Coordinate cross-functional rollout of model changes with engineering, product, and analytics stakeholders
• Mentor junior ML contributors through code reviews and structured growth guidance
• Protect production systems by enforcing guardrails and staged rollouts

Requirements

• Bachelor's or Master's degree in Mathematics, Physics, Computer Science, or a related technical field
• 4–7 years of professional experience in machine learning and production monitoring
• Proven track record of leading complex model development with measurable business impact
• Strong experience with regression, classification, ranking, gradient-boosted trees, and neural architectures
• Proficiency in Python and SQL
• Hands-on experience with big data tools like Spark and various ML libraries
• Experience designing evaluation frameworks and diagnosing feature drift or data quality issues
• Strong grasp of probability, statistics, and experimental design

Preferred Qualifications

• Experience with system programming languages like C++ or Rust
• Hands-on experience with online inference systems, gRPC/REST, or streaming pipelines like Kafka/Flink
• Ad-tech domain knowledge including auction dynamics and fraud detection
• Experience with contextual bandits, multi-armed bandit frameworks, or reinforcement learning
• Familiarity with MLOps tooling such as MLflow, Prefect, or Airflow