AI Systems Engineer

About the Role

We're looking for a senior engineer to co-lead our research and implementation efforts on applying LLMs and other ML techniques to reason about software security. You'll play a critical role in shaping not just the product, but also our technical direction, engineering culture, and team.
We value strong judgment and principled thinking—you'll have the autonomy to challenge assumptions, explore unconventional ideas, and chart the right course between rigor and speed. This role is about navigating ambiguity, making deliberate trade-offs, and pushing the boundaries of what's possible in automated security reasoning.

Responsibilities

• High-level security reasoning: Build systems that analyze architecture and posture to infer risks and generate security requirements and recommendations.
• Tactical security reasoning: Analyze product specs and task descriptions to generate concrete security requirements before code is written.
• Security pattern enforcement: Leverage organizational and codebase context to identify, codify, and enforce security best practices across the SDLC.
• Toolchain integration: Connect with tools like Google Docs, Confluence, Jira, and GitHub to ingest development artifacts.
• LLM & model engineering: Fine-tune, benchmark, and optimize LLMs and ML models for security-specific reasoning tasks.
• Scalability & performance: Design AI systems to efficiently process large-scale codebases and documentation.
• Modern engineering practices: Use infrastructure as code, write robust tests, follow CI/CD workflows, and collaborate in a Kanban process.

Required Skills & Experience

• AI & Machine Learning: 2+ years of experience in AI/ML, preferably with LLMs and autonomous agents.
• Programming & AI Development: Proficiency in Python and AI/ML frameworks (e.g., TensorFlow, PyTorch, LangChain, OpenAI APIs).
• Software Development & DevSecOps: Experience with software development lifecycles, version control (Git), Infrastructure-as-Code (e.g., Terraform) and CI/CD pipelines (e.g., GitHub Actions).
• APIs & Integrations: Familiarity with API development and integrations with developer tools (e.g., Jira, GitHub).
• Software Security: Familiarity with application security, security design principles, and threat modeling methodologies.

Preferred Qualifications

• Experience working with security tooling (e.g., Semgrep, CodeQL, Snyk).
• Knowledge of graph-based reasoning, semantic analysis, or symbolic AI.
• Experience in fintech, healthtech, media, or other regulated industries where security compliance is critical.