Machine Learning Engineer, Causal Inference, Level 5

Jobtailor; California (MO); On-site; USD 150,000 - 210,000; Full time

Job summary

Jobtailor is seeking a senior ML Engineer to design and productionize causal models that quantify impact for users and business value.

You will lead experiments, analyze A/B tests, and collaborate with product and engineering to shape experimentation strategies, balancing complexity and interpretability in production systems.

Candidates should have 5+ years in ML with causal inference expertise and a strong math/stat background.

Qualifications

• Degree in CS/Statistics/Economics or related technical field, or equivalent practical experience.
• 5+ years in machine learning with hands-on causal inference or experimentation.
• Experience building models to inform product decisions using causal techniques.
• Experience designing and analyzing online experiments (A/B tests) and deploying causal ML in production.

Responsibilities

• Design and build models that quantify causal impact and drive business value.
• Develop and productionize causal ML solutions using observational and experimental data.
• Design, analyze, and interpret A/B tests and quasi-experiments with product/engineering partners.
• Evaluate tradeoffs between model complexity, bias/variance, scalability, and interpretability.
• Conduct code reviews and maintain high engineering standards for scalable infra.
• Contribute to rapid iteration cycles while maintaining methodological rigor.

Skills

Causal inference

A/B testing

Experimentation

Machine learning

Product decision support

Education

Bachelor's degree in computer science / statistics / economics or related field

Master's degree in a technical field + 4+ years of ML experience

PhD in a relevant technical field + 2 years of ML experience

Job description

• Design and build models that quantify causal impact, optimize decision‑making, and drive value for users, advertisers, and the business
• Develop and productionize causal machine learning solutions (e.g., uplift modeling, heterogeneous treatment effect estimation) using observational and experimental data
• Design, analyze, and interpret A/B tests and quasi‑experiments; collaborate closely with product and engineering partners to shape experimentation strategies
• Evaluate technical tradeoffs between model complexity, bias/variance, scalability, and interpretability
• Conduct code reviews, maintain high engineering standards, and build scalable, maintainable infrastructure
• Contribute to rapid iteration cycles while ensuring methodological rigor

Requirements

• Bachelor’s degree in computer science, statistics, economics, or a related technical field, or equivalent practical experience
• 5+ years of post‑Bachelor’s experience in machine learning, with hands‑on experience in causal inference or experimentation; or Master’s degree in a technical field + 4+ years of post‑grad machine learning experience; or PhD in a relevant technical field + 2 years of post‑grad machine learning experience
• Demonstrated experience building models to support product decision‑making and policy evaluation through causal techniques
• Experience designing and analyzing online experiments (A/B tests) and leveraging causal ML in production systems