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No credit card required · See jobs in 10 minutes
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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.
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You will work as a Machine Learning Engineer focused on LLM interpretability and systems. CTGT, Inc., a Stanford-born startup backed by Gradient Ventures and Y Combinator, is building a deterministic governance layer for enterprise AI in San Francisco. This role bridges frontier AI research with production systems to tackle non-deterministic risk for Fortune 500 deployments.
Senior Software Engineer in San Francisco, CA, at CTGT, Inc., leading architectural work on a deterministic governance layer for enterprise AI. Backed by Google's Gradient Ventures, General Catalyst, and Y Combinator, CTGT is building the control plane that makes frontier models reliable for Fortune 500 institutions. The role offers total architectural ownership of a high-performance system and.
