The Token Company — ML Researcher

# The Token Company — ML Researcher **Type:** Full-time | On-site | San Francisco, CA **Compensation:** $150,000–$300,000 + 0.5%–1% equity **Hiring count:** 1 **Visa sponsorship:** Yes — H-1B, O-1, OPT **Reports to:** Founder ## About The Token Company The Token Company does LLM interpretability and context-optimization research, building custom machine learning models that analyze and compress token contexts before they reach the underlying model. The result is roughly 50% inference cost reduction, lower latency, and measurably higher accuracy for the enterprises and scale-ups integrating LLMs into their products. Seven months old with roughly 1,000 customers, the company raised $11.7M from First Round Capital and Y Combinator, with additional backing from the founders of Hugging Face, Slack, and Dropbox, and has been through both YC and HF0. Founded: 2025 | Team size: 1–10 (Seed) | Total funding: $11.7M Industry: AI Tools Website: https://thetokencompany.com Office: San Francisco, CA ## Why Candidates Should Join - **Research that ships:** Success is measured by getting a model into a product used by ~1,000 customers, not by publications. You see your work in production quickly. - **Own a frontier problem end-to-end:** Full ownership of a slice of LLM context compression and mechanistic interpretability — hypothesis, data, architecture, training, evals, and production impact. - **High autonomy:** Every researcher directs their own agenda with minimal structure, reporting to the founder. - **Serious backing & pedigree:** $11.7M from First Round Capital and Y Combinator, plus the founders of Hugging Face, Slack, and Dropbox; YC and HF0 alumni. - **Real compute:** Training runs on NVIDIA B200s and large-scale GPU clusters. - **Everything covered:** SF housing, food and meals, laundry and cleaning, healthcare and dental, significant equity, visa sponsorship, resources to build out the research team, and company off-sites. ## Intake Call Summary - No intake call transcript was available on the role page. An **Intake Video** is posted on Contrario but was not transcribed here — review it directly for hiring-manager nuance before scoring borderline candidates. ## The Role As an ML Researcher, you own a slice of one of the most interesting open problems in applied AI: figuring out what information inside an LLM context actually matters, and how to represent it more efficiently. This is a high-autonomy, high-output role for someone who wants to run a large volume of experiments, reproduce papers, and see their research ship into a production system used by real customers. ### What You'll Be Doing - Design and run experiments on LLM context compression and mechanistic interpretability, including model training, data curation, labeling pipelines, and evals - Read current research papers and generate longer-term ideas for representing context more efficiently for LLMs - Own your research direction end-to-end, from hypothesis through training runs on NVIDIA B200s and large-scale GPU clusters to evaluation and production impact - Contribute to the eval infrastructure that measures how model outputs change and how compression affects accuracy and latency - Iterate quickly on new architectures and training methods, treating shipping a model into the product as the primary success condition **Tech stack:** Transformers, custom model training loops (data + architecture + training + evals), NVIDIA B200s and large-scale GPU clusters, eval infrastructure. ## Requirements - Prioritize production impact over publication metrics - Own model training stack including data, architecture, training, evaluation, and shipping - Trained models from scratch, end-to-end ownership of data, architecture, and training loop - Strong ML fundamentals: transformers, mechanistic interpretability, LLM research - High-agency researcher: self-directed, experiment-driven, not RAG or chatbot-only - Spiky profile: exceptional pre-career achievement in competitions, resea…