Forward Deployed Engineer

About the role

This role exists to close the gap between how things actually work and how they're documented, ensuring our platform knows it too. You'll report directly to the CTO and work within Engineering, but your day will look nothing like a typical engineer's.

Responsibilities

• Own the last mile of customer success: ensuring each customer's knowledge, configurations, and processes are set up so that automation works at the right time, on the right things, with the right context.
• Dig into customer environments to extract the tacit, tribal knowledge that lives in people's heads (the stuff that's not in any runbook) and structure it into playbooks and knowledge configurations our AI can act on.
• Evaluate and tune automation performance: figure out what's firing correctly, what's missing, what's producing poor outcomes. When something isn't working, you own it. You don't escalate to engineering and wait. You treat it as an opportunity to improve the system yourself.
• Come to customers with a point of view about their own operations, informed by their data and your experience across other environments. Help them see where their IT processes need to change to get more from automation, not just replicate existing workflows inside our platform.
• Recognize patterns across customers. What you learn in one environment should make the next one faster. Turn repeatable insights into repeatable approaches.
• Close the feedback loop between the field and the product. When you see confusion, friction, or a gap, you don't just work around it. You surface it, document it, and push for it to be fixed at the source.
• Partner with Engineering, Product, Data Science, and Customer Success to translate what you're learning in the field into prioritized improvements that make every future deployment faster.

Requirements

• Experience solving complex, ambiguous problems in customer-facing technical roles. Solutions engineering, technical consulting, implementation engineering, or similar work where you had to learn fast and deliver under pressure.
• Comfort writing code (Python, TypeScript, or similar) and working with APIs. You can build a script, automate a workflow, or push a fix when the situation calls for it. But your first instinct is to reach for knowledge and education before reaching for a code change.
• Familiarity with IT operations. You understand ticketing systems, identity providers, endpoint management, and the kind of enterprise infrastructure that IT teams live in every day.
• Familiarity with AI/ML concepts and a genuine curiosity about how large language models, automation, and knowledge systems can transform service delivery.
• The investigative instinct to pull apart a customer's Okta configs, dig through their logs, trace their ticket history, and piece together why automation isn't doing what it should. Not just what the dashboard says, but what's actually happening underneath.
• A practice of producing artifacts that other people can actually use. Documentation, playbooks, operational recommendations. You know that scaling knowledge is as important as having it.

Qualifications

• Clear communication skills that work across audiences. You can explain a technical architecture to an engineer and explain why a process change matters to a VP of IT, in the same afternoon.
• A genuine belief that every support question is a signal, every moment of customer confusion is a product failure, and every workaround is a feature waiting to be built.
• Comfort operating in a fast-moving startup where your role will evolve as we learn. You're energized by ambiguity, not paralyzed by it.