Machine Learning Engineer 1 - Infra

Overview

Aarki 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.

The role?

We are seeking a motivated and detail-oriented Machine Learning Engineer to join our team. As an ML Engineer, you will help to build and operate the ML systems that power Aarki's programmatic DSP. You will focus on data and model quality, ML observability (MLflow-first), training/serving reliability, and platform health. You'll work closely with Senior MLEs and platform engineers to improve reproducibility, monitoring, and CI/CD for ML, ensuring models are safely trained, evaluated, deployed, and observed at scale.

What will you do?

• Support development of ML platform to streamline model deployment, monitoring and scaling ML solutions
• Collaborate with senior data scientists and cross-functional teams (product, engineering, and analytics) to integrate models into production workflows.
• Build MLOps practices to ensure seamless integration of 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
  
  

• Bachelor's degree in Mathematics, Physics, Computer Science, or a related technical field.
• At least 1 year of professional experience in machine learning and MLOps - ML Lifecycle and deployment
• Experience with machine learning techniques such as regression, classification, and clustering.
• Experienced with ML infrastructure tools (like Kubeflow, MLflow, Airflow, Docker, and Kubernetes) to streamline model deployment, monitoring, and scalable production workflows. [Orchestration tools exposure is an addon]
• 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.
• Strong grasp of probability, statistics, and data analysis principles.
• Exposure to online inference systems, gRPC/REST model endpoints, or streaming features (Kafka/Flink)
• Ad-tech familiarity: auction dynamics, pacing, fraud signals, creative personalization.