Research Scientist

Highlights

• Design experiments for encoding large corpora into compact parametric memory.
• Develop self-study pipelines that let models reflect on new context.
• Shape product used by leading tech and AI companies at frontier of memory.

What they offer

• Competitive cash compensation
• Startup equity

Ideal candidate

You are a pragmatic, collaborative ML researcher passionate about learning and memory who loves working up and down the stack from idea to shipped product and communicating complex ideas clearly.

Requirements

• Deep background in machine learning with fundamentals in inference serving systems, KV cache design, or latency-sensitive model deployment
• Track record of rigorous ML research via publications, strong open-source contributions, or equivalent demonstrated depth
• Extensive experience in at least one relevant area: continual learning, memory architectures, test-time training (TTT), parameter-efficient finetuning, context compression, retrieval, synthetic data, distillation, or agents
• Comfort working up and down the stack from research question to running system, not just handing off ideas
• Strong technical communication with high-bandwidth, generative technical conversation