Artemis
From Artemis's careers page
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Artemis is building the future of AI-driven cybersecurity, helping companies defend themselves effectively in an era where AI is fighting AI on the cyber battlefield.
Our platform brings a new approach to the $100B security operations market, building a live model of each customer's environment and using AI agents to detect, investigate, hunt, and respond to threats at machine speed. Just one year after our founding, Artemis is one of the fastest-growing cybersecurity companies of all time, with customers including multiple Fortune 500 companies, Global 2000 enterprises, and AI innovators like Cursor.
We have raised $70M from Felicis, First Round Capital, Theory Ventures, Two Sigma Ventures, Lockstep VC and Brightmind. We are also backed by some of the most prominent operators in cybersecurity, including the CEO of Crowdstrike, the former CEO and CTO of Splunk, and the founders of Abnormal AI and Demisto.
Artemis's founders have built and scaled security products used by tens of thousands of customers. Shachar Hirshberg (CEO) led Amazon GuardDuty, the world's largest cloud detection product, was an early employee at Demisto, the company that invented the SOAR category, was an engineering leader at Palo Alto Networks, and holds an MBA from Harvard. Dan Shiebler (CTO) led all AI and machine learning at Abnormal AI, the pioneer of behavior-based detection, spent five years building analytics using machine learning and vector databases at Twitter, and holds a PhD in machine learning from Oxford.
Our team of software engineers, AI researchers, security engineers, and product designers comes from Google, Meta, AWS, Wiz, CrowdStrike, SentinelOne, Abnormal AI, Perplexity, Bridgewater, Mercor, CERN, and more.
We're growing fast and looking for builders who want to help us serve our rapidly expanding customer base.
Build scalable adversary emulation – Develop reusable scenarios and automation that reproduce attacker behaviors and multi-stage attack chains across cloud, identity, endpoint, SaaS, AI, and data environments. Prioritize work using threat intelligence, customer risk, and detection coverage gaps.
Validate detections end to end – Trace emulated activity through collection, normalization, enrichment, detection, and investigation. Verify expected findings and evidence, identify missed detections and visibility gaps, and distinguish successful prevention from successful detection.
Research attacks against AI systems – Investigate prompt injection, MCP and tool abuse, retrieval poisoning, excessive agent privileges, and unauthorized actions. Chain weaknesses across applications, agents, and integrations to establish realistic impact and identify detection opportunities.
Research attacks against data planes – Explore abuse of service identities, integration tokens, permissions, and data access across databases, warehouses, object storage, and AI retrieval systems. Turn access, staging, export, and cross-platform attack paths into repeatable scenarios.
Reproduce emerging exploits – Assess relevant vulnerability disclosures and exploit research in isolated labs. Establish prerequisites, practical impact, and observable behavior, then translate findings into detection hypotheses and bounded emulations.
Enable safe testing in customer environments – Build authorized emulations and control checks with clear scope, preflight checks, least-privilege access, execution limits, stop controls, and verified cleanup. Document prerequisites, expected effects, and which scenarios require an isolated lab.
Build continuous validation – Integrate scenarios with detector tests and engineering workflows. Develop AI evaluation harnesses with attack variations, multi-turn tests, and benign controls, measuring outcomes as models, tools, permissions, and detections change.
Turn research into defensive improvements – Contribute detections, hunt logic, telemetry requirements, and regression tests. Partner with engineering and customer owners to drive findings through remediation and retesting, verifying fixes against malicious and benign behavior.
Partner with the SOC and customers – Run purple-team exercises, explain findings and limitations, and give analysts the evidence and guidance needed to investigate emulated behaviors. Use AI to accelerate research and tooling while independently verifying generated actions and conclusions.
5+ years of hands-on cybersecurity experience, with substantial offensive security, red teaming, adversary emulation, or security research work; equivalent demonstrated expertise is welcome.
Strong Python skills and a track record building reusable security tools with APIs, SDKs, Git, code review, and automated testing.
Deep practical expertise in at least one cloud or identity ecosystem, with an understanding of permissions, service identities, sessions, and data access.
Practical web/API security knowledge and experience chaining weaknesses across authorization boundaries into meaningful attack paths.
Experience reproducing attacks in controlled environments and connecting the resulting behavior to audit logs, detection logic, and investigation evidence.
Experience assessing AI applications or agent workflows, or demonstrated offensive research into these systems.
Sound judgment in sensitive environments: define scope, anticipate side effects, limit impact, protect data, and verify cleanup.
Clear communication and the ability to own work from research through implementation, operational use, and verified outcomes.
BONUS
Experience with Atomic Red Team, MITRE ATT&CK and ATLAS, purple teaming, or continuous security validation.
Experience with SQL, detection-as-code, and cloud, identity, SaaS, or endpoint telemetry.
Hands-on work with MCP servers, RAG systems, model gateways, or repeatable AI evaluations.
Experience assessing data warehouses, object storage, Kubernetes, CI/CD systems, or software supply chains.
Contributions to offensive tooling, emulation frameworks, detection content, or published security research.
Experience building isolated labs and enabling customers to operate security tooling.
Make a real-world impact. Your tools and research will directly improve how real companies detect and defend against emerging threats.
Build at the intersection of AI and security. Work with security and AI engineers to test emerging attack surfaces and turn discoveries into practical defenses.
Own what you build. Take ideas from research to working capabilities used across the platform and customer environments.
Work closely with practitioners. Partner with detection engineers, researchers, and analysts who can put your work into use and help measure its impact.
We offer a competitive compensation of $180,000 – $220,000 per year, and a top-of-market equity component. A variety of factors are considered when determining the compensation, including a candidate’s professional experience. Final offer amounts may vary from the amounts listed.
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