Machine Learning Researcher

Position Overview

The Machine Learning Researcher will own cutting-edge research in mechanistic interpretability, context compression, and transformer training. Researchers drive projects end-to-end-from developing hypotheses and curating datasets to training models on large GPU clusters, evaluating results, and deploying models into production. This is a highly autonomous research role where shipping impactful models matters more than publishing papers.

• Design and execute experiments in LLM context compression and mechanistic interpretability.
• Train transformer models from scratch, owning data, architecture, training loops, and evaluation.
• Build datasets, labeling pipelines, and evaluation infrastructure.
• Research and prototype novel model architectures and training methods.
• Deploy successful models into production to improve customer outcomes.
• Read and reproduce current AI research while driving independent research initiatives.

• Experience training machine learning models from scratch with full ownership of data, architecture, and training.
• Strong understanding of transformers and modern deep learning techniques.
• Hands-on experience with large-scale model training and experimentation.
• Research mindset focused on rapid experimentation and production impact.
• High agency and ability to self-direct research.
• Willingness to work onsite in San Francisco in a fast-paced startup environment.

• Experience pretraining transformer models.
• Reinforcement learning or post-training experience for LLMs.
• Novel architecture or training method development with measurable results.
• Experience at frontier AI labs, leading university research groups, or early-stage AI startups.
• Exceptional technical achievements such as Kaggle, ICPC, IOI, ISEF, or similar competitions.
• Strong background in applied mathematics, computer science, or engineering.

• $150,000 - $300,000 base salary depending on experience
• Equity

• H-1B sponsorship available through employer