Machine Learning Engineer: LLM Interpretability & Systems
About the role
Senior ML engineer role focused on mechanistic interpretability and deterministic governance for LLMs. Build production-grade systems that improve model behavior and enable real-time, auditable control in enterprise environments.
Responsibilities
- 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.
Qualifications
- 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.
Skills
Benefits
- Competitive base compensation, plus significant equity in a venture-backed company with institutional investors including Google’s Gradient Ventures, General Catalyst, and Y Combinator.
- Real Impact: work directly on core systems that determine model performance in real-world environments.
- Autonomy & Trust: operate with a high degree of trust and form strong technical opinions.
About CTGT
CTGT safeguards against reputational risk from GenAI across your org
Enterprises are paralyzed by the risks of deploying generative AI. Traditional methods like RAG and fine-tuning are brittle, expensive, and fail to prevent costly hallucinations or guarantee compliance in high-stakes environments. This lack of trust stalls innovation and blocks ROI. CTGT ensures strict, auditable adherence to your policies on the fly. We deliver defensible audit trails and remediate noncompliant model behavior, so outputs become more aligned with company policies over time, combating data and model drift. CTGT is backed by Google's AI fund (Gradient), General Catalyst, and luminaries like Paul Graham. We provide the control and confidence necessary to unlock the full potential of enterprise AI.
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