Senior Staff Software Engineer, Serving

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. At Liftoff, we’re solving one of the core problems faced by every mobile app: growth. To do so, we build Machine Learning and Big Data-driven technology that can accurately predict which apps a user will like, and connect them in a compelling way. Our systems operate at a scale unseen outside of the largest Internet companies -- processing over 12 million requests per second and interacting with over 5 billion unique users each day. The Serving team builds and maintains the mission-critical infrastructure responsible for serving ads across Liftoff’s product line. As an engineer on Liftoff’s Accelerate Serving team, you will: Design, build, and operate the high-throughput, low-latency services that receive bid requests, execute Liftoff’s bidding logic, and respond to ad exchanges in real time. Improve the performance, scalability, and reliability of systems that process millions of requests per second under strict latency constraints. Develop and optimize GPU-powered inference services that execute neural network models using TensorRT. Profile end-to-end inference pipelines to identify and eliminate bottlenecks in GPU utilization, batching, memory access, serialization, networking, and request handling. Partner with machine learning engineers to productionize new model architectures and features, translating modeling requirements into efficient and reliable serving implementations. Design and operate large feature store fleets that provide low-latency access to real-time and precomputed features. Develop benchmarking and performance-analysis tools that help engineers compare models, understand latency and throughput trade-offs, and identify regressions before deployment. Improve experimentation tooling that allows machine learning teams to orchestrate offline training runs, evaluate candidate models, and maintain model leaderboards. Own changes through their full lifecycle—from system design and implementation to testing, deployment, observability, capacity planning, incident response, and continued optimization. Requirements: Strong core Computer Science fundamentals (data structures, algorithms, system architecture) 10+ years of industry experience M.S.. or higher in Computer Science (or equivalent work experience) Experience with Go is a plus Location: This role is eligible for full-time remote work in one of our entities: CA, CO, ID, IL, FL, GA, MA, MI, MN, MO, NJ, NV