Research Engineer - Causal AI

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