Manager, Machine Learning Engineering, Causal Ad Measurement
Job Description
Snap Inc is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company’s three core products are Snapchat , a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio , an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles .
Snap Inc. is a technology company. We believe the camera presents the greatest opportunity to improve the way people live and communicate. Snap contributes to human progress by empowering people to express themselves, live in the moment, learn about the world, and have fun together. The Company’s three core products are Snapchat , a visual messaging app that enhances your relationships with friends, family, and the world; Lens Studio , an augmented reality platform that powers AR across Snapchat and other services; and its AR glasses, Spectacles .
Snap Engineering teams build fun and technically sophisticated products that reach hundreds of millions of Snapchatters around the world, every day. We’re deeply committed to the well-being of everyone in our global community, which is why our values are at the root of everything we do. We move fast, with precision, and always execute with privacy at the forefront.
We’re looking for a Machine Learning Engineering Manager to join the Causal Measurement team, part of the monetization team at Snap!
What you’ll do:
- Lead a team of Causal machine learning engineers, data scientists and software engineers in developing and optimizing systems to conduct lift measurement and incrementality optimization
- Define the overall architecture of the system, ensuring scalability, performance, and reliability
- Conduct lift analysis to evaluate the effectiveness of marketing campaigns, product changes, and other interventions
- Drive innovation in experimental design by implementing and optimizing techniques like hypothesis testing, variance reduction, Bayesian inference, multi-arm bandits, synthetic control, and counterfactual frameworks with robust estimation methods
- Advocate for best practices in causal inference, experimental design, and end-to-end pipeline optimization across the organization
- Chair the Experimentation Council and collaborate with cross-functional teams to ensure high-quality, actionable insights through rigorous A/B testing, uplift modeling, quasi-experimental designs, and advanced causal inference methods
- Collaborate with senior leadership and cross-functional teams to translate complex data insights into strategic recommendations
- Perform methodology, design and code reviews to raise technical excellence bar
Knowledge, Skills & Abilities:
- Deep understanding of causal measurement approaches, algorithms and their application to lift measurement
- Experience setting the direction for teams focused on developing experiment based measurement
- Strong management and mentorship skills, fostering a collaborative and innovative team culture
- Excellent verbal and written communication skills, with meticulous attention to detail
- Ability to effectively collaborate with stakeholders at all levels, both internally and externally
- Proficiency in managing and solving ambiguous problems
Minimum Qualifications:
- Bachelor’s in a related technical field such as computer science or equivalent years of experience
- 8+ years of machine learning industry experience
- 2+ years of experience leading machine learning teams
Preferred Qualifications:
- Deep understanding of experimental design, A/B testing, and causal inference methods (e.g., propensity score matching, instrumental variables, regression discontinuity)
- Familiarity with techniques like hypothesis testing, variance reduction, Bayesian inference, multi-arm bandits, synthetic control, and counterfactual frameworks with robust estimation methods
- Experience with large-scale data sets and big data technologies
- Experience with ad ecosystem and working with cross-functional stakeholders
- Experience working with distributed systems
- Experience working with machine learning, ranking infrastructures, and system designs
- Ability to proactively learn new concepts and apply them at work
If you have a disability or special need that requires accommodation, please don’t be shy and provide us some information .
“Default Together” Policy at Snap: At Snap Inc. we believe that being together in person helps us build our culture faster, reinforce our values, and serve our community, customers and partners better through dynamic collaboration. To reflect this, we practice a “default together” approach and expect our team members to work in an office 4+ days per week.
At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. EOE, including disability/vets.
Our Benefits : Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success!
Compensation
In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate’s starting pay will be determined based on job-related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.
Zone A (CA, WA, NYC) :
The base salary range for this position is $222,000-$333,000 annually.
Zone B :
The base salary range for this position is $211,000-$316,000 annually.
Zone C :
The base salary range for this position is $189,000-$283,000 annually.
This position is eligible for equity in the form of RSUs.
