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# Machine Learning Researcher

- **Company:** HHM
- **Location:** San Francisco, CA, United States
- **Salary:** $150,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Clusters, Machine Learning, Reinforcement Learning, Large Language Models, Deep Learning, Kaggle, Information Technology, Machine Learning Operations
- **Published:** July 26, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=4cb7bde1a33eeca5

## About the Role

* 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.

## Description

HHM Talent is assisting a client in their search for a Machine Learning Researcher in San Francisco, CA., 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.


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