Machine Learning Engineer: LLM Interpretability & Systems

About the role

The Senior Machine Learning Engineer will operate deep within the model stack, working directly with weights, activations, and architectures to build the systems that make AI governance deterministic. The mandate is to determine how a model can be improved for a specific purpose and build the systems that operationalize that within our platform.

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.

Requirements

• A strong understanding of Transformer architectures, PyTorch internals, and the mathematical foundations of deep learning.
• Experience training, fine-tuning, or optimizing models beyond superficial augmentation.
• Able to read a paper, decide what matters, and implement it.
• Able to notice when something is not working and take ownership of fixing it.

Qualifications

• Motivated by the challenge of making large language models reliable and controllable enough for the highest-stakes enterprise applications.

Skills

• Strong understanding of Transformer architectures, PyTorch internals, and the mathematical foundations of deep learning.
• Experience training, fine-tuning, or optimizing models beyond superficial augmentation.
• Able to read a paper, decide what matters, and implement it.
• Able to notice when something is not working and take ownership of fixing it.

Benefits

• Competitive base compensation.
• 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 the core systems that determine how models perform in the wild.
• Autonomy & Trust: Operate with a high degree of trust and form strong technical opinions and execute on them.

Pay

• $175 - $250 per hour

Schedule

• Full-time