## **About CTGT & The Mission**
Despite massive investment in commercial AI, organizations often find that demonstrated value is elusive, primarily due to the non-deterministic risk inherent to generative models. CTGT is the deterministic governance layer that enables the most important global institutions to deploy AI workflows with confidence.
Born out of Stanford University research, we provide the control plane that makes it possible. A lightweight, model-agnostic system that enforces policy, prevents drift, and produces auditable decisions in real time. When benchmarked on HaluEval, the CTGT Policy Engine (paired with GPT-120B OSS) outperformed several frontier models with 80% lower compute cost.
While we sit on the edge of AI research, CTGT brings frontier intelligence into real-world environments. We apply cutting-edge theory directly in production to make large language models more reliable, controllable, and performant in practice.
Our mission is to bring models to the level of performance and accountability required by the Fortune 500. By bridging the gap between LLM capabilities and domain-specific requirements, we unlock the true potential of generative AI to solve the most pressing problems in our world today.
## **The Role**
CTGT is building governance infrastructure for frontier intelligence. Your job is to make sure it deploys effortlessly, runs reliably at scale, is easy to reason about under pressure, and delightful to build on.
You will own the systems that connect our Policy Engine and core model work to production environments. That includes how services are structured, how they are deployed, how they are observed in production, and how they behave when things go wrong.
This role sits at the boundary where most systems break. Between backend logic and infrastructure, between experimental systems and production guarantees. You are expected to operate comfortably in that space and improve it over time.
## **What You Will Do**
* Build and operate systems that serve AI workloads in critical business environments
* Own how services are deployed, configured, and run across cloud and on-prem environments
* Define infrastructure through code and enforce consistency across environments
* Establish strong observability across the stack, including metrics, logs, and tracing that actually help debug real issues
* Design deployment pipelines and runtime environments that support rapid iteration without breaking production
* Work with ML engineers to turn experimental systems into reliable, production-grade services
## **Who You Are**
* You have owned production systems and know what breaks under real load
* You move comfortably between backend and infrastructure without treating them as separate domains
* You have strong experience with at least one major cloud platform
* You use tools like Terraform as a means to control systems, not as an end in itself
* You have built observability that actually helps diagnose issues, not just dashboards
* You take responsibility for system behavior and fix problems at the root
## **What We Offer**
**Compensation & Equity**: Competitive base compensation, plus significant equity in a venture-backed company with institutional investors including Google’s Gradient Ventures, General Catalyst, and Y Combinator. We want people who think and act like owners.
**Real Impact:** You will work directly on the core systems that determine how models perform in the wild. Your work ships into real, high-stakes environments where governance, audit-ability, and performance are non-negotiable.
**Autonomy & Trust:** We operate with a high degree of trust. You are expected to form strong technical opinions and execute on them.
Visa sponsorship is offered for this role.
