AI Engineer

We’re hiring AI engineers from early career to Staff levels to build the intelligence that helps enterprises understand and act on AI risk. You’ll develop practical systems that evaluate AI agents, their identities and permissions, the data they can access, and their activity—turning complex signals into useful, explainable risk insights. You’ll apply LLMs and other AI techniques where they improve outcomes, combining model capabilities with reliable data, graph context, and deterministic controls. You’ll own work end to end, from shaping the problem and designing evaluations through implementation, deployment, and iteration, with a focus on accuracy, reliability, latency, and cost.

Must-haves

• 3+ years building and shipping production software, AI systems, or ML-backed products (we’ll calibrate scope and level to experience)
• Strong applied AI judgment: you can identify where AI adds value, choose an appropriate approach, and measure whether it works
• Experience building or integrating AI/ML capabilities into software, with a practical understanding of evaluation, failure modes, and production tradeoffs
• Solid software engineering fundamentals, including APIs, data modeling, testing, and cloud-based systems
• Ability to balance quality, latency, and cost when designing AI-powered features
• A thoughtful approach to security, privacy, reliability, and responsible handling of customer data

Helpful experience

• Building with LLMs, including structured outputs, retrieval, tool use, or model evaluation
• Developing risk scoring, classification, entity resolution, or other intelligence from complex or connected data
• Python, TypeScript, PostgreSQL, Azure, or similar technologies
• Cybersecurity, identity, permissions, data security, or enterprise AI systems
• Production observability and feedback workflows for improving AI system quality
• Using AI coding tools thoughtfully as a high leverage part of your engineering workflow

You don’t need to match every preferred qualification to apply. We’ll calibrate scope and level during the process.

• Customer-focused: You care about solving real customer problems and use feedback to improve the product.
• Analytical and pragmatic: You make decisions with evidence, choose the simplest sound solution, and avoid unnecessary complexity.
• Fast, thoughtful execution: You like learning by shipping, iterating, and taking responsibility for outcomes.
• Low ego, high collaboration: You communicate directly, welcome feedback, and help teammates succeed.
• Comfortable with startup ambiguity: You can adapt as priorities shift and contribute wherever the team needs you.

• Ownership and growth — drive problems end to end and learn in a high velocity environment.
• Frontier technology — technically challenging work that both leverages and secures the latest advancements in AI.
• Early stage opportunity — make a real impact and have meaningful equity in the outcome.
• Category defining cybersecurity — AI is a new kind of security challenge; be part of shaping how it’s solved.