About Cogent Security
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
Cogent Research is the applied AI lab at the heart of Cogent's mission. As an AI Research Engineer, you will work at the frontier of automated reasoning and AI-driven code analysis — inventing novel solutions, prototyping fast, and pivoting when first ideas don't pan out.
This is not a role for those who need a playbook. We are looking for someone who thrives in ambiguity, drives work proactively and independently, and is deeply aligned with Cogent's mission to make the world's infrastructure safer.
What You’ll Do & Achieve
- Conduct original research at the intersection of AI and automated code reasoning, advancing the state of the art in areas such as static/dynamic analysis, formal verification, and automated testing
- Invent and apply novel AI techniques to challenges in code reasoning and code generation, from early hypothesis through working prototype
- Design and execute automated testing and fuzzing strategies to validate and stress-test systems under development
- Navigate ambiguity with a proactive mindset — adapt quickly when initial approaches don't pan out and iterate toward robust solutions
- Publish research findings at top-tier conferences and peer-reviewed journals, contributing to the broader scientific community
- Collaborate with engineering and product teams to translate research breakthroughs into Cogent's core AI platform
- Stay on top of cutting-edge developments in ML and program analysis, and propose incorporating them into Cogent's systems where appropriate
What You’ll Bring
- Experience in Automated Reasoning: Hands-on experience with static/dynamic analysis, formal verification, and/or automated testing and fuzzing
- AI Expertise: A track record of developing or applying AI/ML techniques to code reasoning or code generation problems
- Research Publication: Demonstrated research output — papers accepted at recognized conferences or peer-reviewed journals
- Education: Degree in Computer Science or a closely related technical field (BS, MS, or PhD)
- Independence: A highly self-directed, proactive, and self-starting attitude — you identify problems, propose solutions, and execute without being told
- Mission Alignment: A genuine passion for Cogent's mission and a belief that this work matters
Bonus Points For:
- Experience in cybersecurity — knowledge of vulnerability classes, exploit techniques, or secure-by-design systems
- Familiarity with Cloud Infrastructure as Code (IaC) — tools like Terraform, Pulumi, CDK, or equivalent
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.
