ML Researcher - LLM Context Optimization

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

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• 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
  
  

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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

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• 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