Compa builds compensation benchmarking software that pulls pay data from HR, recruiting and equity systems instead of relying on surveys. Its stack pairs a Django/FastAPI and React/TypeScript product with a full ML toolchain (PyTorch, XGBoost, MLflow, Prefect, vLLM, Sentence Transformers), and senior-skewed, engineering-heavy hiring is accelerating around an agent harness and ML-enabled data pipelines.
Compa, founded in 2020 and headquartered in Newport Beach, California, provides compensation intelligence software for enterprise compensation and total rewards teams. The platform ingests data from HCM, ATS and stock administration systems to deliver current benchmarks for cash, equity, offers and skills-based pay, with privacy controls built in. It also uses AI agents to support compensation analysis. The company is privately held, employs 51–200 people and hires in the United States.
Compa runs Python with Django and FastAPI, React, TypeScript and GraphQL, on AWS (including EKS), GCP and Azure with Docker and Kubernetes. ML tooling includes PyTorch, XGBoost, scikit-learn, MLflow, Prefect and vLLM.
Yes. Engineering dominates Compa's accelerating hiring, mostly at senior level, with smaller sales and executive openings, all in the United States.
Job posts point to building an agent harness, integrating ML and AI into data workflows, and building data pipelines for its compensation platform.
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Compa'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 →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.
