Machine Learning Engineer - L3

Rzr

Bengaluru, IN

On-site

Who this role is best for

Best suited to mid-level ML engineers with 1-3 years of experience in Python and SQL, working in ad-tech or programmatic advertising.

Best fit for

Candidates with applied ML experience in programmatic advertising will align well.
— “predicting user responses, forecasting bid landscapes, and detecting fraud”
Engineers comfortable with big data tools like Spark and ML libraries.
— “familiarity with big data tools (e.g., Spark) and ML libraries”
Those who thrive in cross-functional collaboration with product and engineering teams.
— “Collaborate with senior data scientists and cross-functional teams”

Things to consider

Expect involvement in both model development and production pipeline maintenance.
— “Build and maintain data pipelines to process and prepare large datasets”
Documentation of experiments and reproducibility is a key requirement.
— “Document experiments, assumptions, and outcomes; maintain reproducibility”

How to stand out

Highlight any experience with online inference systems or streaming features.
— “Exposure to online inference systems, gRPC/REST model endpoints”
Showcase projects where you integrated new data sources into models.
— “Analyze the impact of integrating new data sources and features”
Demonstrate ad-tech knowledge, especially auction dynamics or fraud signals.
— “Ad-tech familiarity: auction dynamics, pacing, fraud signals”

Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · TeamLevel · Mid

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

developed and implemented machine learning models
integrated models into production workflows
analyzed impact of new data sources
maintained data pipelines
tested new tools and methodologies

Typical background

bachelor’s degree in mathematics, physics, computer science1-3 years of professional experience in machine learning

Skills & requirements

Required

Machine LearningPythonSQLBig Data ToolsMl LibrariesProbabilityStatisticsData AnalysisTeamworkCommunication

Preferred

C++RustGrpcRestKafkaFlinkAd-techAuction DynamicsPacingFraud SignalsCreative Personalization

Stack & domain

PythonSQLSparkTensorFlowPyTorchscikit-learnCommunicationFinanceHealthcare

About the role

Original posting from Rzr via Greenhouse

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.
 

Source: Rzr careers (Greenhouse)