Research Engineer - Causal AI
Department: Engineering
Location: San Francisco HQ
Compensation: $200K – $250K • Offers Equity • Multiple Ranges
Employment Type: FullTime
About Alembic
Alembic is where top engineers are solving marketing's hardest problem: proving what actually works. If you're looking for frontier technical challenges at an applied science company, this is the place.
At Alembic, we're not just building software—we're decoding the chaos of modern marketing. Join Alembic to build trusted systems that Fortune 100 companies use to make multimillion-dollar decisions. We're backed by leading tech luminaries including WndrCo (founded by DreamWorks founder Jeffrey Katzenberg), Jensen Huang, Joe Montana, and many more.
About the Role
We're looking for an Applied Scientist who solves hard mathematical problems in marketing attribution through both algorithmic innovation and production-quality implementation. You'll design novel approaches to measurement challenges, implement them as production systems, and work directly with customers to ensure statistical rigor at enterprise scale.
This role is ideal for someone who wants to apply deep technical expertise to real-world problems—shipping code that makes a difference, not just publishing papers.
What You'll Do
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
What Will Help You Succeed
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
Why You Might Be Excited About Alembic
Hard problems with real impact: You'll tackle the hardest challenges in marketing analytics while building systems that influence multimillion-dollar decisions at Fortune 100 companies
Technical autonomy: You want ownership over technical decisions and the freedom to solve complex problems your way
Cutting-edge technology: Work with advanced AI/ML algorithms, composite AI solutions, private NVIDIA DGX clusters, and the latest in data processing at scale
Elite team: Join top engineers who thrive on challenging problems and high-impact work
Startup upside: Early-stage equity opportunity with experienced leadership and proven product-market fit
Why You Might Not Be Excited
If you only want to tell people what to build instead of building and coding alongside them, we're not the environment for you
You prefer company practices with 100% built-out process for every detail
You prefer static over dynamic. Projects, priorities, and roles will adapt to your skill set and goals. Though we have real paying customers and a playbook for growth, we proudly remain an early-stage startup
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