Alembic
Research Engineer - Causal AI
9mo ago
200000 –250000 USD / yearUSASeniorcausal inferencealgorithm designstatistical analysispythonproduction systems
Research Engineer working on causal AI for marketing measurement and production systems.
Responsibilities
- 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.
- 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
Nice to have
- 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
Other
- 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.
- 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
- 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
- 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
- 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