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, research, or founding SF in-person, 996 intensity, hacker-house environment Green Flags Pretrained a transformer model Serious post-training or RL experience on transformers Built novel architecture or training method with results Shipped trained models into production systems Experience in research labs, startups, or scale-ups Exceptional early-career achievement Red Flags Experience mostly in RAG, agents, or prompt engineering Primary focus on fine-tuning existing models through APIs Preference for publishing papers over shipping models Work-life balance as a stated priority Role Details Salary — $150,000–$300,000 (above $300K possible for exceptional candidates) Equity — 0.5%–1% Experience — 2+ years On-site policy — On-site in San Francisco; hacker-house environment; ~996 pace (9am–9pm, six days/week) or more Visa sponsorship — H-1B, O-1, OPT Employment type — Full-time Location — San Francisco, CA Screening Questions Phone number Are you allowed to work in the United States? LinkedIn / Personal website Note: The Required Candidate Q&A section on the role page was collapsed ("Show More") — additional screening questions may exist beyond these three. Confirm the full list in Contrario before submitting. Interview Process Stage 1 — Pending Approval — Candidates awaiting initial approval. Stage 2 — Otso screen — Initial screen. Stage 3 — Second person technical — Technical interview with a second team member. Stage 4 — Whole team — Interview with the whole team. Stage 5 — Take home / Work trial — Practical work trial. Stage 6 — Offer — Offer extended. Stage 7 — Hired — Candidate accepts and starts. Ideal Companies & Backgrounds Updated July 16, 2026 — drawn from the role's Nice-to-Have list; no separate Contrario "Ideal Companies" section was present on the page. University AI labs / frontier labs — Stanford AI Lab (SAIL), Berkeley AI Research (BAIR), or a frontier AI lab Competitive / exceptional pre-career achievement — Kaggle Grandmaster, IOI or ISEF medalist, ICPC finalist, or similar Early-stage experience — Prior startup experience, founding experience, or a technical lead role at an early-stage company Technical pedigree — Applied math, CS, or engineering background from a university with strong technical pedigree Model work — Has pretrained a transformer, done serious post-training or RL on one, or built a novel architecture with results