Former DeepMind Researchers' Startup Mirendil Raises $1B Series A to Automate AI Model Training

Mirendil, an AI infrastructure startup co-founded by former Google DeepMind and Anthropic researchers Behnam Neyshabur and Harsh Mehta, announced it has raised $1 billion in Series A funding. According to SiliconANGLE, the round was jointly led by Kleiner Perkins and Clearlake Capital, with participation from more than six other investors including Google LLC. Neyshabur is a co-inventor of SAM, an algorithm that improves AI model performance on tasks they were not specifically trained for, while Mehta previously worked on pre-training projects at DeepMind and Anthropic.

The company's flagship product, Lor, is a platform designed to automate the trial-and-error phases of model training. Mirendil states that Lor uses hundreds of AI agents to handle code writing, infrastructure setup, algorithm selection, and data collection. The company cited an example where a single researcher used Lor to build a competitive coding model in 3.5 months for $10 million, with over 99% of the code generated by AI agents.

Building on this technology, Mirendil launched a specialized coding model called Fern last month. The company claims Fern can process 10 million input tokens per prompt, approximately ten times the capacity of Claude Opus 5.5, equivalent to about half a million lines of code. Fern is based on a version of Mirendil-Base with 100 billion to 500 billion parameters. The company also previewed upgraded versions ranging from 500 billion to 10 trillion parameters, planning to use these algorithms to develop more capable LLMs. Specific post-money valuation terms remain undisclosed.