Machine Learning Engineer

Liftoff

36d•$215,000 - $275,000•Remote

About The Position

Liftoff is a leading AI-powered performance marketing platform for the mobile app economy. Our end-to-end technology stack helps app marketers acquire and retain high-value users, while enabling publishers to maximize revenue across programmatic and direct demand. Liftoff’s solutions, including Accelerate, Direct, Monetize, Intelligence, and Vungle Exchange, support over 6,600 mobile businesses across 74 countries in sectors such as gaming, social, finance, ecommerce, and entertainment. Founded in 2012 and headquartered in Redwood City, CA, Liftoff has a diverse, global presence. About the Revenue Engine team The Revenue Engine team works to understand the fundamental economics of the mobile ad tech marketplace, including the elasticity of demand and the effects of competition. The team of machine learning engineers, software engineers, and data analysts develops theories, validates those theories with experiments and analyses, and uses the learnings to build production systems that improve outcomes for Liftoff and its advertisers. As a Machine Learning Engineer on the Revenue Engine team, you will: Build statistical models and production systems to balance advertiser performance with business goals. Tune optimization parameters, measure internal competition, and model dynamic environments. Design and run experiments to validate theories underpinning the mobile ad tech economy. Develop applications in the areas of advertiser budget retention and growth, optimal margin allocation, and bidding innovations. Collaborate with a team of world-class engineers with diverse backgrounds as well as peers across the broader company (e.g. Operations, GTM). Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences. Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and design/code review.

Requirements

PhD in Computer Science, Machine Learning, Economics, or a related field.
Industry experience applying economics or machine learning to large scale problems.
Solid engineering and coding skills.
Excellent team communication and collaboration skills.

Nice To Haves

Experience with ad tech is a solid plus.

Responsibilities

Build statistical models and production systems to balance advertiser performance with business goals.
Tune optimization parameters, measure internal competition, and model dynamic environments.
Design and run experiments to validate theories underpinning the mobile ad tech economy.
Develop applications in the areas of advertiser budget retention and growth, optimal margin allocation, and bidding innovations.
Collaborate with a team of world-class engineers with diverse backgrounds as well as peers across the broader company (e.g. Operations, GTM).
Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences.
Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and design/code review.