Machine Learning Engineer - SF On-Site

About Us : 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 comprehensive audience engagement platform includes creative strategy and execution. With over 14 years in the industry, we handle 5 million mobile ad requests per second from over 10B 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 Applied Scientist to join our team. As an Applied Scientist, 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 research scientists and other team members to drive impactful data science projects and contribute to innovative solutions.

This is an on-site role, 5 days a week, based in our South Beach office in San Francisco, California.

Join us in pushing the boundaries of AI and mobile advertising in a collaborative environment that fosters creativity and growth. We offer a competitive salary, comprehensive benefits, and significant opportunities for career advancement.

Role & Responsibilities

• Develop and deploy machine learning models at scale to address key challenges in programmatic advertising, such as user response prediction, bid landscape forecasting, and fraud detection.
• Conduct exploratory data analysis and apply statistical techniques to extract insights and support decision-making.
• Build and maintain data pipelines to ensure efficient processing and integration of large-scale data for model training and evaluation.
• Work closely with senior data scientists and cross-functional teams including product, engineering, and business units to integrate models into production systems and applications.
• Assist in the development and implementation of best practices for model deployment, monitoring, and performance assessment.
• Stay updated on recent developments in machine learning and data science, and apply relevant techniques to solve complex problems and enhance our platform.
• Contribute to the exploration and adoption of new methodologies and technologies to advance our data science capabilities.

Skills & Experience