Head of Machine Learning

RZR Global Inc.; San Francisco (CA); On-site; USD 150,000 - 200,000; Full time

Job summary

RZR Global Inc. is seeking a Head of Machine Learning in San Francisco to lead development of their next-generation machine learning platform. In this role, you will design and build the production ML system, work directly in the codebase, and lead the ML team.

The position requires strong programming skills, experience in building production systems, and leadership capabilities. A background in advertising technology is a plus. This is an opportunity to shape systems and teams from the ground up.

Qualifications

• 7+ years of experience with a focus on building production machine learning systems.
• Experience taking ML models from research to production at scale.
• Strong programming background in Python and at least one systems language.

Responsibilities

• Design and build the next generation production ML system.
• Work directly in the codebase to prototype models and ship production systems.
• Build and lead the machine learning team.

Skills

Production machine learning systems

Python

Systems languages (C++, Java, Rust, Go)

Large-scale data pipelines

Communication skills

Tools

Spark

ClickHouse

AeroSpike

Prefect

Job description

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 Head of Machine Learning to lead the development of the next generation of our machine learning platform. This role combines technical leadership, hands‑on development, and organizational building. You will be responsible for the infrastructure for training and serving the deep learning models powering our Demand Side Platform.

Our machine learning systems power real‑time advertising use cases including bidding, ranking, pacing, and fraud detection. These models operate in extremely latency‑sensitive environments, serving millions of predictions per second. You will work closely with engineering and product leadership to translate business challenges into scalable ML solutions.

This role is ideal for someone who enjoys building systems and teams from scratch, moving quickly, and owning outcomes end‑to‑end.

Key Responsibilities

Design and build the next generation production ML system, including training pipelines, feature pipelines, and real‑time inference services.

Work directly in the codebase to prototype models, evaluate approaches, and ship production systems.

Build and lead the machine learning team, including hiring, mentoring, and establishing engineering culture.

Partner with engineering to design infrastructure capable of serving millions of low‑latency predictions.

Identify high‑impact ML opportunities across bidding, ranking, pacing, fraud detection, and optimization.

Establish best practices around experimentation, model evaluation, monitoring, and continuous improvement.

Translate business and product goals into measurable machine learning improvements.

Required Skills / Experience

7+ years of experience with a focus on building production machine learning systems.

Experience taking ML models from research or prototype to production at scale.

Strong programming background in Python and at least one systems language (C++, Java, Rust, or Go).

Experience working with large‑scale data pipelines and training infrastructure.

Comfortable operating in early‑stage environments where you may be both the architect and the implementer.

Strong product intuition and ability to prioritize ML work based on business impact.

Experience scaling ML teams.

Excellent communication skills and ability to work across engineering, product, and leadership.

Nice‑to‑Have

Experience in advertising technology and real‑time systems.

Experience operating ML infrastructure in on‑prem environments using open‑source tools including Spark, ClickHouse, AeroSpike, and Prefect.