Causal AI platform for marketing attribution and revenue forecasting
Alembic Technologies builds a causal inference platform that ties marketing spend to revenue, starting with attribution and forecasting for enterprises. The stack pairs CUDA and C++ with Spark, Kafka and Airflow across AWS, GCP and Azure, pointing to GPU-heavy modeling on multi-cloud data pipelines. Hiring is accelerating and entirely senior, with job posts stressing platform reliability, incident response and replacing correlation-based attribution.
Alembic Technologies is a privately held company founded in 2018 and headquartered in San Francisco, California. It develops a causal AI platform built on graph neural networks that helps enterprises measure how their actions, initially advertising spend, drive revenue. The platform supports attribution, revenue forecasting and insight generation across brand, performance and omnichannel budgets. The company employs 51–200 people and hires in the United States.
Alembic uses Python, C++ and CUDA alongside Apache Spark, Kafka, Airflow, Snowflake, BigQuery and PostgreSQL, running on AWS, GCP and Azure with Kubernetes, Terraform, ArgoCD and Datadog.
Job posts point to field studies on real customer data, production systems for causal inference, enterprise platform implementations, data integration for causal analysis, and monitoring and incident response.
Yes. Engineering leads its hiring, which is accelerating and focused on senior roles in the United States, with additional openings in data, research, HR, legal and support.
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Alembic Technologies's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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