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AlembicAlembicSan Francisco, CA

Research Engineer - Causal AI

Designs and implements novel causal inference algorithms and production systems for marketing attribution challenges at enterprise scale. Requires 5+ years shipping research code, strong math/stats background, Python proficiency, and customer collaboration.

200k – 250k/yr
On-site5+ YOEML Engineering

About the role

Responsibilities

  • Design and implement novel approaches to marketing measurement problems, shipping working code
  • Build production systems for causal inference that maintain statistical rigor at enterprise scale
  • Develop algorithms that are both mathematically sound and computationally efficient
  • Collaborate with customers to understand their measurement challenges and develop technical solutions
  • Create tools and libraries that enable both internal teams and customers to leverage advanced analytics
  • Document research and implementation decisions for reproducibility and knowledge transfer

Requirements

Applied Science & Engineering

  • 5+ years developing and shipping research code in production environments
  • Strong mathematical background - statistics, probability, optimization, causal inference
  • Proficient Python developer - can write production-quality code, not just notebooks
  • Causal inference expertise - practical experience applying causal methods to real problems
  • Data-intensive systems - experience processing and analyzing large datasets
  • Research to production - track record of turning research ideas into shipping features
  • Communication skills - can explain complex technical concepts to varied audiences

Domain & Advanced Skills

  • MS or PhD with significant applied research experience
  • Background in econometrics, statistics, or computational social science
  • Experience in marketing analytics, A/B testing, or measurement domains
  • Understanding of ML engineering and MLOps practices
  • Ability to work directly with customers on technical problems
  • Experience with both Bayesian and frequentist statistical methods

Nice to Haves

  • Published applied research or technical writing
  • Experience in consulting or customer-facing technical roles
  • Background in operations research or decision sciences
  • Familiarity with GPU computing and performance optimization
  • Understanding of privacy-preserving analytics and differential privacy

Skills

PythonCausal InferenceStatisticsProbabilityOptimizationMl EngineeringMLOpsEconometricsA/B TestingBayesian Methods

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