Machine Learning Researcher - Mechanistic Interpretability & Context Compression
Machine Learning Researcher responsible for cutting-edge research in mechanistic interpretability and context compression, owning end-to-end projects from hypothesis development to production deployment. Must design experiments, train transformer models from scratch, build datasets and evaluation infrastructure, and deploy impactful models. Requires strong transformer expertise, large-scale training experience, and high agency in a fast-paced San Francisco startup environment.
Key Highlights
Key Responsibilities
Technical Skills Required
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Job Description
HHM Talent is assisting a client in their search for a Machine Learning Researcher in San Francisco, CA.
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.
Responsibilities
- 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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- 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.
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- 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.
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- $150,000 - $300,000 base salary depending on experience
- Equity
- H-1B sponsorship available through employer
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