Staff Machine Learning Engineer

Role overview Build and lead the machine learning capability behind automated revenue optimization for mobile advertising. As the first dedicated ML engineer, you will take models from problem definition through production serving and evaluation, replacing manual monetization controls with systems that work within customer constraints and improve long-term value. The role combines hands-on engineering, product collaboration, and technical leadership.

Responsibilities - Design, train, evaluate, and deploy models for revenue optimization, owning the lifecycle from feature design to online measurement. - Make practical architecture decisions about modeling, training, and serving, and implement those decisions in production systems. - Develop approaches such as contextual bandits, policy learning, or gradient-boosted models where suitable. - Work with product colleagues and customers to understand optimization goals and constraints, then shape the roadmap accordingly. - Establish direction and engineering standards for the ML discipline, and help hire and mentor future team members as it grows. - Measure business outcomes through real production experiments and use findings to guide iteration.

Requirements - A track record of deploying machine learning models in production request paths, serving live traffic under latency and reliability constraints. - Concrete evidence of measurable business or revenue impact from a system you built, including how the impact was evaluated. - Experience maintaining consistency between training and serving features and addressing training-serving skew. - Strong hands-on skills in model development and production ML engineering; this is an individual contributor role that involves regular coding. - Ability to work with services written in Go, including reading serving code and contributing fixes when needed. - Strong technical judgment and communication to collaborate with product, engineering, and customers.

Nice to have - Experience with auction pricing, real-time bidding research or systems, and tools such as XGBoost, LightGBM, or ONNX. - Familiarity with AWS, ClickHouse, Kubernetes, and production model observability.

Benefits and work setup The role is remote with flexible hours and few synchronous meetings. Benefits include founding-team equity, medical, dental, and vision coverage, and a hardware budget. The stated US base salary range begins at $300,000.