Machine Learning Engineer (Causal Inference)

TL;DR

Machine Learning Engineer (Causal Inference): Designing and building models to quantify causal impact and optimize decision-making for users and advertisers with an accent on uplift modeling and heterogeneous treatment effect estimation. Focus on developing production-ready causal ML solutions and designing complex A/B tests to drive business value.

Location: Hybrid (Must be based in Los Angeles, New York, Palo Alto, or San Francisco); office attendance required 4+ days per week

Company

Technology company operating hirify.global, focusing on visual communication and empowering people to express themselves.

What you will do

• Design and build models to quantify causal impact and optimize decision-making for users and advertisers.
• Develop and productionize causal ML solutions, including uplift modeling and heterogeneous treatment effect estimation.
• Design, analyze, and interpret A/B tests and quasi-experiments.
• Evaluate technical tradeoffs between model complexity, bias/variance, scalability, and interpretability.
• Build scalable, maintainable infrastructure and maintain high engineering standards through code reviews.

Requirements

• 5+ years of post-Bachelor's experience in machine learning, with hands-on experience in causal inference or experimentation.
• Bachelor’s degree in computer science, statistics, economics, or a related technical field.
• Proficiency in Python and common ML libraries (pandas, NumPy, scikit-learn, CausalML).
• Experience building models to support product decision-making and policy evaluation through causal techniques.
• Experience designing and analyzing online experiments (A/B tests) in production systems.

Nice to have

• Advanced degree (MS/PhD) in a quantitative field such as statistics, data science, or operations research.
• Experience with causal inference libraries like EconML or DoWhy.
• Deep understanding of intent-to-treat (ITT) vs. ghost ad methodologies.
• Expertise in Bayesian inference for decision-making under uncertainty.

Culture & Benefits

• Comprehensive medical coverage and emotional/mental health support programs.
• Paid parental leave.
• Compensation packages that include sharing in the company's long-term success.
• Collaborative "default together" culture with a strong emphasis on in-person interaction.