CTGT

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. 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 A new open-source model is released and you are compelled to reach inside and understand how it actually works. You instinctively try to push it beyond what most people say is already impressive. You observe model behavior and don’t think, “What’s a better prompt?”, but “How do I improve its fundamentals?” CTGT’s Senior Machine Learning Engineer will operate deep within the model stack, working directly with weights, activations, and architectures to build the systems that make AI governance deterministic. Your work powers the Policy Engine, the core technology that gives enterprises real-time, auditable control over model behavior in production. Your mandate is ostensibly simple but difficult in execution: determine how a model can be improved for a specific purpose and build the systems that operationalize that within our platform. As opposed to simply using models, you will probe the mechanics of their cognition. What You Will Do Take ideas from mechanistic interpretability and related work and turn them into code that runs in production, making research into reality. Work directly with model internals to improve behavior and performance across commercial and open-source models. Leverage techniques like activation patching, control vectors, and feature extraction to achieve targeted, repeatable improvements in model output. Build the evaluation and deployment loops needed to ship changes reliably into enterprise environments. Design and optimize the feature-level intervention systems that enable deterministic policy enforcement at inference time. Who You Are Strong understanding of Transformer architectures, PyTorch internals, and the mathematical foundations of deep learning. Have trained, fine-tuned, or optimized models beyond superficial augmentation. Can read a paper, decide what matters, and implement it. Notice when something is not working and take ownership of fixing it. Motivated by the challenge of making large language models reliable and controllable enough for the highest-stakes enterprise applications. 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, auditability, 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.

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 frontier models (Gemini 3 Pro Preview, Claude 4.5 Opus and 4.5 Sonnet) at drastically 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's mission is to deploy high-performance model governance at enterprise scale. This places rigorous requirements on our system. Our Policy Engine must produce decisions that are correct, fast, and reliably auditable, you will ensure it stays that way as load grows and the platform expands into new environments. This standard is set everywhere in the system, not at a single layer: where governance decisions are computed and persisted, where correctness has to hold under concurrent load, and where early design choices either compound into leverage or into debt. This role is for the engineer who owns the system. You will make the architectural decisions that shape how the platform evolves, and you will write the code that proves those decisions were right. The work rewards strong judgment about what to build, what to defer, and what to throw away. It demands the ability to hold a large system in your head and keep it coherent as it grows. We are looking for someone whose strength is the fundamentals of software engineering, applied at the level of real systems. The engineer other engineers want next to them when something hard needs to be built correctly the first time. What You Will Do Design and build the core services, deciding how the system is decomposed, where state lives, and how components communicate Work wherever the problem leads, from data model to product surface Make deliberate tradeoffs across accuracy, consistency, availability, and latency Define the internal APIs and abstractions that determine system trajectory 6+ months out Who You Are You have built non-trivial systems and can speak honestly about what aged well and what did not You understand distributed systems tradeoffs well enough to make the right call for a given problem You are comfortable on both sides of the stack and choose where to work based on the problem You have strong opinions about software design and can defend them without being precious about them 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, auditability, 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.

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.

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 the control plane for frontier intelligence. This is the layer where users understand, configure, and trust how our systems behave. Your job is to make that layer clear, reliable, and intuitive. You will design and build the product interfaces through which customers interact with the Policy Engine and our governance platform. This is not about presentation. It is about giving users real control over complex systems without exposing unnecessary complexity. You will define reusable patterns and components that can be applied across products, ensuring consistency as the platform grows. When the right solution requires changes beyond the interface, you will make them. This role sits at the boundary between product experience and system behavior. The quality of that boundary determines whether users trust the system or fight it. What You Will Do Build products interfaces that give users clear control over complex AI systems Own the full stack behind those interfaces, from interaction through to backend behavior Design and implement APIs and services that support clean, predictable product surfaces Develop reusable components and patterns that scale across the platform Ensure consistency, correctness, and performance across the entire user experience Who You Are You have built and owned full stack systems that users rely on You are strong on both frontend and in designing and building backend systems You think in terms of product surfaces and system behavior, not just components or endpoints You have experience with modern frontend frameworks and understand how they work under the hood You design systems that stay coherent as complexity grows You take responsibility for the end-to-end user experience 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, auditability, 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.