Machine Learning Engineer, Experimentation

Within Monetization (Ads), the Experimentation team is responsible for Measurement Statistics, Measurement Correctness, and Measurement Infrastructure, which underpins model-based experimentation. Every change to our models is deployed via an experiment and measured for improvements to quality (for our 3Bn+ monthly active users) and value (for millions of advertisers). We are at the heart of Meta's growth, driving significant step changes in a a domain operating at significant scale.We are seeking an experienced and mathematically skilled Machine Learning Engineer to join our Experimentation team. This is a leadership hire focused on solving high-impact business challenges in experimentation. You will advance our approach on how we approach launch decisions, optimize launch models, and significantly reduce uncertainty in our experimentation. This role offers an opportunity to solve open and challenging problems and deliver a step change in ML Experimentation., Machine Learning Engineer, Experimentation Responsibilities:
1. Drive the development of new adaptive experimentation techniques
2. Lead efforts to improve the sensitivity and trustworthiness of our experimentation platform
3. Develop solutions for open problems at the cutting edge of experimentation measurement, addressing challenges such as selection bias due to user opt-outs
4. Collaborate closely with Core ML research scientists and product teams (e.g., Instagram) to ensure seamless integration and avoid revenue interference
5. Apply deep mathematical and statistical expertise to build robust models based on opt-in user data
6. Contribute to a team that ensures the quality of ads meets defined benchmarks for users, while also delivering maximum value for advertisers
7. Help connect businesses to people through better, more relevant advertisements

8. Mathematical and statistical background, including probability and statistical theory, including probability and statistical theory
9. Experience with Python programming
10. Experience with data analysis across large datasets
11. Experience with software engineering combined with statistical and mathematical expertise
12. Experience navigating large enterprise architectures, identifying issues and implementing nuanced changes, 13. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
14. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
15. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
16. Experience with Machine Learning, with emphasis on statistical and mathematical foundations
17. Demonstrated ability to integrate AI tools to optimize workflows and drive measurable impact
18. Experience with experimentation platforms and A/B testing infrastructure
19. Track record of staying current with the latest research advancements in experimentation and measurement