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About the Role

We are building a world model for cybersecurity, starting with threat modeling. Our system autonomously models attack paths, validates them, and remediates vulnerabilities.

Responsibilities

• Wire up MCP servers shared across agent swarms, bridging SSE transports with STDIO proxies.
• Build and extend agent workflows within a bespoke agent SDK.
• Design integrations that trigger on git changes, cloud deployments, or ticketing events for autonomous remediation.
• Package and deploy models as hardened, IP-safe containers.
• Tune high-throughput inference pipelines for structured training data generation.
• Build the observability and telemetry layer for measuring and improving system efficacy.
• Harden the product itself to ensure security.

Requirements

• Natural systems thinker who reasons about data flow, failure modes, and architecture.
• Learns by building and can create working prototypes with new SDKs or protocols.
• Comfortable operating across the stack from model inference to container networking to API design.
• Cares about the entire lifecycle of what they build: observation, testing, scaling, and trust.
• Thinks adversarially by default.

Skills

• TypeScript
• Python
• PostgreSQL
• Docker
• Kubernetes networking
• Hybrid deployment patterns

Location

• Remote

Work Type

• Full-time

Experience Level

• Mid-level
• Senior

About the Company

• We are building a world model for cybersecurity, starting with threat modeling.
• We are building an autonomous threat modeling system that rigorously models attack paths against large-scale infrastructures, orchestrates agents to validate each path, and remediates confirmed vulnerabilities at every step of the attack chain.
• We are hiring a team of engineers to help us scale this from a sharp early product into something that runs autonomously across real-world environments: from git repos to cloud deployments to bug bounty programs.
• Cybersecurity is stuck in a reactive loop: scan, alert, patch, repeat. We're building something that breaks that cycle by reasoning proactively about how systems fail.