Machine Learning Engineer

About the Company

CTGT is the deterministic governance layer that enables global institutions to deploy AI workflows with confidence. Born out of Stanford University research, we provide a lightweight, model-agnostic system that enforces policy, prevents drift, and produces auditable decisions in real time.

Responsibilities

• Turn ideas from mechanistic interpretability and related work into production-ready code
• 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 for targeted improvements
• Build evaluation and deployment loops to ship changes reliably into enterprise environments
• Design and optimize feature-level intervention systems for deterministic policy enforcement at inference time

Requirements

• Strong understanding of Transformer architectures and PyTorch internals
• Deep knowledge of the mathematical foundations of deep learning
• Experience training, fine-tuning, or optimizing models beyond superficial augmentation
• Ability to read research papers and implement relevant findings
• Proven ability to take ownership of technical challenges and fixes

Benefits

• Competitive base compensation
• Significant equity in a venture-backed company
• High degree of autonomy and trust