---
title: 'Machine Learning Engineer, Infra (MLOps) at RZR Global Inc.'
canonical: 'https://feeny.ai/job/machine-learning-engineer-infra-mlops-rzr-global-inc-beijing-y3s35t2kgar7'
type: 'job'
last_seen: '2026-09-10'
---

# Machine Learning Engineer, Infra (MLOps) at RZR Global Inc.

- **Company:** RZR Global Inc.
- **Location:** Beijing, China
- **Work type:** hybrid
- **Posted:** 2026-09-01
- **Last confirmed live:** 2026-09-10
- **Apply:** https://job-boards.greenhouse.io/rzr/jobs/4390098009

## Job description

Who Are We?

RZR is an AI-native advertising platform built for the next era of performance marketing. We operate at the intersection of machine learning, programmatic media, and full-funnel mobile growth, powering campaigns for some of the world's most ambitious advertisers. Our platform is purpose-built to deliver outcomes at scale, not just impressions.

We are a team of builders, operators, and technologists who believe the advertising industry is overdue for a fundamental rethink. We move fast, operate with a high degree of ownership, and hold ourselves to an exceptionally high standard of craft.

RZR is scaling aggressively with an active M&A pipeline and a platform vision that puts us on a path to becoming an industry leader. This is a rare opportunity to join a company at an inflection point and help shape what it becomes.

## Role Overview

As Machine Learning Engineer (Infra / MLOps) at RZR, you will design, build, and operate the model training and deployment infrastructure that powers our Demand-Side Platform (DSP). This role focuses on building scalable, flexible, and reliable systems for training models on billions of records across bidding, ranking, pacing, and fraud use cases.

You will work at the intersection of machine learning, data platforms, and infrastructure — with a strong focus on automation, reproducibility, and reliability. This is a P0 priority hire directly tied to accelerating RZR's migration from legacy model training systems to Prefect-based DNN pipelines, enabling 100% UA on DNN.

The right person for this role combines production-grade ML systems experience with a strong bias to automate, document, and build for reliability — someone who takes end-to-end ownership from data to serving, and is energized by the complexity of high-QPS real-time bidding infrastructure.

## Key Responsibilities

- Own the development and evolution of infrastructure that enables faster, more reliable, and more cost-efficient model training
- Design, build, and maintain automated model training and orchestration pipelines that scale across large datasets and support rapid recovery from failures
- Develop standardized training workflows that support experimentation, reproducibility, versioning, and traceability
- Build and operate observability and monitoring systems to detect data quality issues, training instabilities, model anomalies, and performance regressions
- Improve the efficiency, scalability, and maintainability of the model training codebase, defining and enforcing best practices across the ML organization
- Apply DevOps and MLOps best practices to machine learning training workflows, including CI/CD and automated testing
- Design, develop, and continuously optimize ML infrastructure for advertising recommendation systems, covering model training, online inference, model serving, and feature pipelines
- Build a high-performance, highly scalable ML platform to support rapid iteration and stable deployment of advertising recommendation models
- Optimize distributed training, online inference, and resource scheduling to continuously improve system performance, stability, and resource utilization
- Collaborate closely with algorithm engineers to drive efficient implementation of recommendation, ranking, and ad-serving models
- Stay current with advancements in ML infrastructure and AI technologies, including the application of LLMs in recommendation and advertising scenarios

Required Skills and Experience

Must-Have

- Strong proficiency in Python and Spark for ML training and deployment workflows
- Experience building and operating machine learning pipelines in production environments
- Hands-on experience with DevOps practices including CI/CD, infrastructure as code, and automated testing
- Experience with workflow orchestration tools such as Airflow or Prefect for ML pipelines
- Solid understanding of ML experimentation, reproducibility, model versioning, and dataset management
- Experience with large-scale data pipelines, feature generation, and offline/online data consistency
- Experience developing recommendation systems, advertising systems, search systems, or machine learning platforms
- Familiarity with mainstream ML frameworks such as PyTorch and TensorFlow
- Experience with ML infrastructure, model training, online inference, or model serving

Nice-to-Have

- Familiarity with system programming languages including C++ and Rust
- Strong grasp of probability, statistics, and data analysis principles
- Exposure to online inference systems, gRPC/REST model endpoints, or streaming features via Kafka or Flink
- Ad-tech familiarity: auction dynamics, pacing, fraud signals, creative personalization
- Experience with large-scale distributed training, high-performance computing (HPC), or GPU optimization
- Familiarity with distributed computing frameworks such as Kubernetes, Ray, Spark, and Flink
- Interest in or practical experience with LLMs and their application in recommendation and advertising scenarios
- Experience with on-prem deployments of open source tools including Spark, ClickHouse, and Redash
- Strong English reading and writing skills for collaboration with global teams

Why Join RZR?

- End-to-end ownership across the full ML stack — data, features, training, evaluation, serving, A/B testing, and monitoring. You will not be working on one slice of the pipeline; you will shape all of it.
- Real-time bidding and training pipelines at genuine scale — high QPS with tight latency SLOs. The infrastructure challenges here are not academic.
- Shape the MLOps platform from the ground up — you will drive observability, data and model quality systems, and the MLflow-first platform, with direct influence on how the ML organization operates.
- Mentorship and structured growth — paired with a senior ML engineer, with structured growth goals and a strong code review culture.
- Immediate, measurable impact — your work will directly accelerate model iteration speed, improve feature quality, and improve offline/online metric alignment for RZR's core bidding and ranking systems.
- Exposure to emerging AI technologies — RZR is actively exploring LLM applications in recommendation and advertising, and this role sits at the center of that work.

RZR Behaviors

RZR operates by eight core behaviors: Extreme Ownership · Move Fast · Drive for Excellence · Proactive Communication · Courage · Curiosity · Deliver Results · Manage Ambiguity

## About RZR Global Inc.

## Company Overview
- **One-liner**: RZR is an AI‑supervised growth marketing intelligence platform that unifies mobile user acquisition, retargeting, CTV, and influencer campaigns into a single performance system.
- **Entity Type**: Private (no funding round disclosed)
- **Headquarters**: San Francisco, California, United States
- **Founded**: Not publicly available; the company has been operating for over a decade (previously known as Aarki)
- **Founders**: Not publicly available

## Core Business
- **Primary industry**: Advertising Services / Programmatic AdTech
- **Target customers**: B2B – mobile app marketers, gaming studios, consumer brands, entertainment, food & beverage, retail
- **Mission statement**: “To make brands impactful at scale.”

## Products & Services
- **[Encore](https://www.rzr.com/encore/encore)**: Proprietary AI‑supervised machine learning bidding engine. Uses multi‑stage deep neural networks to predict install probability, purchase probability, and expected lifetime value at the impression level. Powers bid decisions across mobile UA, retargeting, and CTV.
- **User Acquisition**: Identifies and acquires high‑LTV users before competitors bid on them, optimizing across SKAN, Android, and real‑time attribution environments.
- **Retargeting**: Re‑engages lapsed users with the highest probability to return, spend, and stay active.
- **CTV**: Reaches audiences in the living room with full‑screen creative and feeds warm audiences into mobile activation.
- **Influencer**: Activates social proof at scale; performance data feeds back into the optimization loop.

## Market Standing
- **Valuation / Market Cap**: Not disclosed
- **Key Metric**: 65 employees (as of 2026); four owned‑and‑operated data centers processing 6M+ queries per second
- **Notable Investors / Partners**: Not publicly available
- **Growth Signals**: Global expansion with offices in San Francisco, New York, London, Bangalore, Beijing, Manila, and Seoul; recent rebrand from Aarki to RZR signals strategic evolution toward a multi‑channel, retention‑led platform.

## Competitive Advantages
- **Proprietary neural architecture** built on over a decade of performance data
- **AI‑supervised machine learning** – human analysts monitor calibration, reallocate budgets, and direct traffic toward highest‑value cohorts, keeping models aligned to real business goals
- **Cross‑channel learning** – audience signals, bidding logic, and performance learnings carry across mobile UA, retargeting, CTV, and influencer campaigns
- **Owned infrastructure** – four data centers give full control over latency, scale, and data privacy
- **iOS privacy resilience** – uses contextual signals and synthetic data to maintain prediction accuracy without IDFA

## Strategic Focus
- **Retention‑led growth** across every channel, optimizing for lifetime value rather than single events
- **Connected intelligence** – unifying mobile, TV, and social into one learning system
- **Expanding CTV and influencer** as high‑attention channels that feed back into the mobile performance loop
- **Continued investment in AI/ML** to compound performance over time

## Why Work Here
- **Culture**: “Intelligence is measured by the impact it creates.” The company emphasizes hiring smart people who make intelligent decisions.
- **Global team**: Offices in 7 countries across North America, Europe, Asia, and the Middle East – opportunities for cross‑cultural collaboration.
- **Engineering focus**: Deep work in neural architecture, real‑time bidding, large‑scale data processing (6M+ queries/sec), and multi‑stage ML models.
- **Remote / hybrid**: Not explicitly stated; given multiple office locations, a hybrid or office‑based model is likely.
- **Perks**: Not publicly detailed, but the company highlights a “connected team delivering retention‑led growth at global scale” and a mission‑driven environment.

## Sources
1. [RZR Homepage](https://www.rzr.com/)
2. [RZR About Page](https://www.rzr.com/about)
3. [RZR Product Page](https://www.rzr.com/product)
4. [RZR Encore Page](https://www.rzr.com/encore/encore)
5. [RZR LinkedIn](https://www.linkedin.com/company/rzr)

## Other roles at RZR Global Inc.

- [Growth Manager, Americas](https://feeny.ai/job/growth-manager-americas-rzr-global-inc-united-states-z5jtsq17sgh1) — United States
- [Accounting Manager, China](https://feeny.ai/job/accounting-manager-china-rzr-global-inc-beijing-64xf4tv09mp6) — Beijing, China
- [Regional Director of Revenue, InSEA](https://feeny.ai/job/regional-director-of-revenue-insea-rzr-global-inc-bangalore-rx9559k4718b) — Bangalore, India
- [Director of Marketing  - China](https://feeny.ai/job/director-of-marketing-china-rzr-global-inc-beijing-8yk1vb80y68a) — Beijing, China
- [Senior Learning & Development](https://feeny.ai/job/senior-learning-development-rzr-global-inc-las-vegas-nevada-emn0v9k87wq7) — Las Vegas Nevada, United States
- [Admin and Workspace Experience Manager](https://feeny.ai/job/admin-and-workspace-experience-manager-rzr-global-inc-bengaluru-b7csrgab0spt) — Bengaluru, India
- [Senior / Lead Analyst- PH (L4)](https://feeny.ai/job/senior-lead-analyst-ph-l4-rzr-global-inc-manila-amytby6mcbwj) — Manila, Philippines
- [Associate Analyst (L2)](https://feeny.ai/job/associate-analyst-l2-rzr-global-inc-metro-manila-d4159c587r45) — Metro Manila, Philippines
- [Analyst- PH (L3)](https://feeny.ai/job/analyst-ph-l3-rzr-global-inc-manila-vx1t48sypwam) — Manila, Philippines
- [Growth Manager - CN](https://feeny.ai/job/growth-manager-cn-rzr-global-inc-beijing-jncn5b43xm32) — Beijing, China