Alembic
Research Engineer - Causal AI
San Francisco HQ$200k–$250kfulltimemidAdded 1 month ago
About this role
Alembic seeks a Research Engineer to develop causal inference systems that solve marketing attribution challenges for Fortune 100 companies. You'll design novel algorithms, implement production-quality code, and work directly with customers to ensure statistical rigor at enterprise scale.
What you'll do
- Design and implement novel causal inference approaches for marketing measurement problems
- Build and maintain production systems that handle statistical rigor at enterprise scale
- Develop mathematically sound and computationally efficient algorithms
- Collaborate with customers to understand measurement challenges and deliver solutions
- Create analytics tools and libraries for internal and customer use
- Document research and implementation decisions for reproducibility
What they're looking for
- Python production engineering and research code development
- Causal inference and statistical methods (Bayesian and frequentist)
- Marketing analytics, A/B testing, and measurement domains
- Data-intensive systems and large-scale data processing
- ML engineering and MLOps practices
- Technical communication and customer collaboration
- Optimization and probability theory
- GPU computing and performance optimization (nice to have)
Benefits
- Tackle high-impact problems influencing multimillion-dollar decisions
- Technical autonomy and ownership over complex problem-solving
- Access to cutting-edge technology including private NVIDIA DGX clusters
- Work with elite engineering team focused on challenging problems
- Early-stage startup equity with proven product-market fit
- San Francisco HQ location
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Alembic
Alembic is an AI platform that develops causal inference systems to solve marketing attribution challenges for enterprise customers. The company is hiring for infrastructure engineering and research engineering roles to build scalable systems and novel algorithms.
- Website
- alembic.com
Likely interview questions
- Walk us through a time you took a research idea from concept to production code. What were the biggest challenges in bridging that gap?
- Describe your experience applying causal inference methods to real-world problems. What methods have you used and what made them suitable for those specific problems?