Software Engineer - Data Platform (Staff+)

ABOUT COGENT

Cogent is an Applied AI Lab building the next generation of AI agents for cybersecurity. AI has fundamentally changed how attacks happen, allowing malicious actors to operate at unprecedented speed and scale. Cogent’s "AI Taskforce" assesses petabytes of enterprise data to remediate these issues before critical breaches occur.

To stay at the cutting edge, we blend frontier research with real-world execution. Alongside our core product work, Cogent Research serves as our applied AI lab, providing the research horsepower needed to make truly agentic security workflows a reality.

Since coming out of stealth, Cogent has experienced rapid growth. We partner with Fortune 500 companies to secure some of the most complex production environments in the world.

We’re backed by Greylock and we’ve built a team with the best minds in applied AI. Our team is comprised of people from:

- Top universities like Stanford, Berkeley, Penn, Duke, Carnegie Mellon, Waterloo

- Unicorn, high-growth companies like Scale AI, Databricks, Stripe, Tesla, Coinbase

- World class cybersecurity experts from Wiz, Abnormal AI, Zscaler

- Preeminent research labs like Deepmind and SAIL

ABOUT THE ROLE

We are looking for Founding and Staff-level Engineers to design and implement the foundational pillars of Cogent’s data platform and integration pipeline in order to reliably ingest and normalize enterprise data sources across many corporate and IT systems and then to enable the transformation of that data into a knowledge base that can be leveraged by other GenAI systems.

WHAT YOU’LL DO & ACHIEVE

- Build the new standard for ingesting and extracting enterprise data

- Your role will involve building data connectors, running data pipelines, and extracting insights and context from our customer’s enterprise environments.

- You should get excited about building robust and secure backend systems that handle large volumes of data, ETLs, as well as reimagining how enterprise security teams should extract, store and access data.

- Design highly performant data pipelines and indexing strategies that enable Applied AI use cases like semantic search and retrieval augmented generation

- Work with Applied AI teams to taxonomize, correlate and deduplicate data across various enterprise sources, and architect indexing, storage and query strategies for data in Cogent’s data lake.

- Architect the modern data platform for Gen AI

- The LLM stack and Gen AI landscape is changing everyday. You’ll be front and center of making build vs buy decisions for various tools and services needed by the data platform, and integrating these decisions into our broader architecture and tech roadmap.

- Implement generalizable design patterns and system components that allow us to scale from 1 integration to thousands of integrations

WHAT YOU’LL BRING

- 5+ years of professional experience as a hands-on engineer and technical leader leading multiple projects

- Experience building data pipelines for Information Retrieval Systems (such as knowledge graphs, search engines, or similar) for enterprise use cases

- Experience in ETL orchestration and workflow management tools (such as Temporal, Airflow) as well as building batch processing and streaming systems with tools like Apache Kafka/Flink

- Expert in database fundamentals, SQL, data reliability practices and distributed computing

- Experience working fluently with standard orchestration & deployment technologies like Kubernetes, Terraform, Docker, Databricks, etc in multiple clouds

FOR CALIFORNIA BASED APPLICANTS

The standard base salary range for this position is $100,000 - $300,000 annually. Compensation offered will be determined by factors such as location, job level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.

We are committed to building an inclusive and diverse company. We do not discriminate based on gender, ethnicity, sexual orientation, religion, civil or family status, age, disability, or race.