Machine Learning Engineer: LLM Interpretability & Systems at CTGT, Inc.

As a Machine Learning Engineer at CTGT, Inc., you will work deep within the model stack to make generative AI deterministic and reliable. You’ll apply mechanistic interpretability techniques to model weights and activations, building the policy engine that enforces real-time governance for high-stakes enterprise applications at the edge of AI research.

• Operationalize mechanistic interpretability research into production-ready code that improves model behavior through direct internal interventions.
• Design and optimize feature-level intervention systems that enable real-time, auditable policy enforcement during model inference.
• Build robust evaluation and deployment loops to ensure model changes are reliably shipped into complex enterprise VPC environments.

• Possesses deep expertise in Transformer architectures, PyTorch internals, and the mathematical foundations required for advanced deep learning optimization.
• Demonstrates a proven track record of training or fine-tuning models beyond simple augmentation, specifically probing the mechanics of model cognition.
• Exhibits strong technical ownership with the ability to translate academic papers into high-performance Python, Rust, or TypeScript implementations.

• Bridge the gap between frontier AI research and real-world production by implementing mechanistic interpretability techniques like activation patching and control vectors.
• Join a high-growth team backed by elite investors including Google’s Gradient Ventures, General Catalyst, and Y Combinator with significant equity upside.
• Develop the core deterministic governance layer required by the world's largest institutions to safely deploy generative AI at scale.