ML Researcher David Joseph & Company San Francisco, CA Job Details Full-time $150,000 - $300,000 a year 4 hours ago Benefits Visa sponsorship Dental insurance Qualifications AI models Generative models AI platforms (beyond public GPTs) Machine intelligence Model deployment Developing large-scale AI models Model training Model evaluation Generative AI Full Job Description San Francisco, CA • On-site •

Full-time Compensation:

$150,000-$300,000 + 0.5%-1% equity About the Company A seed-stage AI research lab and infrastructure provider working on LLM interpretability and context optimization. The team builds custom machine learning models that analyze and compress token contexts before they reach the underlying model, cutting inference costs by roughly 50% while lowering latency and improving accuracy for the enterprises and scale-ups that integrate LLMs into their products. Founded in 2025, the company already serves roughly 1,000 customers. Founded 2025 • 1-10 people (Seed) •

Industry:

AI Tools 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 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 Why Join Research that ships into a production system used by ~1,000 customers — impact over publications Full end-to-end ownership of a frontier problem in LLM context compression and interpretability High autonomy: every researcher directs their own agenda Training runs on NVIDIA B200s and large-scale GPU clusters SF housing, food, laundry/cleaning, healthcare and dental, significant equity, visa sponsorship, and company off-sites

Details Location:

San Francisco, CA Work policy: On-site; hacker-house environment; ~996 pace (9am-9pm, six days/week)

Compensation:

$150,000-$300,000 + 0.5%-1% equity Visa sponsorship:

H-1B, O-1, OPT

Employment type: Full-time