Senior Machine Learning Engineer

Who are we?

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. 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 looking for a Senior Machine Learning Engineer who can independently lead complex model development, drive measurable business impact, and raise execution standards across the team. You will own end-to-end delivery of models and systems - from problem framing and data pipeline design through training, evaluation, deployment, and monitoring - within our programmatic DSP. You will also coordinate cross-functionally, mentor junior contributors, and protect production stability while pushing the frontier on model quality and iteration speed. In addition to your primary focus area, you will support the team on conversion models as needed.

Key Responsibilities

• Lead development of complex ML models that directly impact business KPIs (campaign selection, conversion prediction).
• Design and own scalable evaluation frameworks — offline metrics, backtesting, and online A/B analysis — ensuring reliable decision-making.
• Own deployment of new models, tools, and architectural improvements end-to-end, including rollback plans and monitoring.
• Strengthen real-time monitoring systems to detect performance degradation early and trigger appropriate responses.
• Anticipate scaling constraints in training infrastructure and serving latency; proactively address bottlenecks.
• Improve inference latency without degrading model performance.
• Coordinate cross-functional rollout of model changes with engineering, product, and analytics stakeholders.
• Eliminate recurring sources of prediction instability through permanent systemic fixes.
• Mentor junior ML contributors through code reviews, pairing, and structured growth guidance.
• Protect production systems from unstable experimentation by enforcing guardrails, validation checks, and staged rollouts.
• Drive measurable uplift through structured model iteration — hypothesis-driven experiments with clear success criteria.

Required Skills / Experience

• 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, including end-to-end model development, deployment, and production monitoring.
• Proven track record of leading complex model development that delivered measurable business impact.
• Strong experience with ML techniques: regression, classification, ranking, gradient-boosted trees, and neural architectures.
• Proficiency in Python and SQL; hands-on experience with big data tools (Spark) and ML libraries
• Experience designing evaluation frameworks and diagnosing training/serving gaps, feature drift, and data quality issues.
• Experience coordinating cross-team rollouts and influencing execution standards beyond direct output.
• Strong grasp of probability, statistics, and experimental design (A/B testing, causal reasoning).
• Excellent communication skills — able to explain technical tradeoffs to diverse stakeholders and maintain composure during conflict or ambiguity.

Nice-to-Have

• Experience with system programming languages (C++, Rust) and contributing to inference-layer
• Hands-on experience with online inference systems, gRPC/REST model endpoints, or streaming feature pipelines (Kafka/Flink).
• Ad-tech domain knowledge: auction dynamics, pacing algorithms, fraud detection, creative personalization, bid landscape modeling.
• Experience with contextual bandits, multi-armed bandit frameworks, or reinforcement learning in production.
• Familiarity with MLOps tooling: experiment tracking (MLflow), orchestration (Prefect/Airflow), and model registries.