Machine Learning Engineer: LLM Interpretability & Systems

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.