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Salary: USD 100,000 - 300,000 / annual
Cogent is an Applied AI Lab building next-generation AI agents for cybersecurity. The company's "AI Taskforce" assesses petabytes of enterprise data to identify and remediate security issues before critical breaches occur. Cogent blends frontier research with real-world execution, partnering with Fortune 500 companies to secure complex production environments. The company is backed by Greylock and has assembled a team with expertise from top universities (Stanford, Berkeley, Penn, Duke, Carnegie Mellon, Waterloo), high-growth companies (Scale AI, Databricks, Stripe, Tesla, Coinbase), leading cybersecurity firms (Wiz, Abnormal AI, Zscaler), and preeminent research labs (DeepMind, SAIL).
You will design and implement foundational pillars of Cogent's data platform and integration pipeline. Your responsibilities include:
- Build the new standard for ingesting and extracting enterprise data by creating data connectors, running data pipelines, and extracting insights from customer enterprise environments. You'll develop robust, secure backend systems handling large data volumes and ETLs, reimagining how enterprise security teams extract, store, and access data.
- Design highly performant data pipelines and indexing strategies enabling Applied AI use cases like semantic search and retrieval-augmented generation. You'll work with Applied AI teams to taxonomize, correlate, and deduplicate data across enterprise sources, architecting indexing, storage, and query strategies for Cogent's data lake.
- Architect the modern data platform for GenAI by making build-vs-buy decisions for tools and services needed by the data platform, integrating these decisions into broader architecture and tech roadmap. You'll implement generalizable design patterns and system components enabling scaling from 1 to thousands of integrations.
REQUIREMENTS:
- 5+ years of professional experience as a hands-on engineer and technical leader leading multiple projects
- Experience building data pipelines for Information Retrieval Systems (knowledge graphs, search engines, or similar) for enterprise use cases
- Experience with ETL orchestration and workflow management tools (Temporal, Airflow) and batch processing/streaming systems (Apache Kafka, Flink)
- Expert-level knowledge of database fundamentals, SQL, data reliability practices, and distributed computing
- Fluency with standard orchestration and deployment technologies (Kubernetes, Terraform, Docker, Databricks) across multiple clouds
