Context
Entering a new phase of our journey, we recently unveiled a new project called LaVague, focused on the broader goal of democratizing AI use to automate daily tasks. LaVague automates manual tasks by translating natural language instructions into seamless browser operations, simplifying what is often seen as mundane or time-consuming tasks. This project leverages our AI and cybersecurity expertise to ensure that these automation pipelines handling users' sensitive data remain private. We are currently seeking world-class AI interns to help scale this ambitious project.
Learn more about LaVAGUE here.
Aside from developing only cybersecurity components for others like OpenAI, Mithril Security is now extending its focus towards creating solutions aimed directly at end-users.
LaVague is enhancing internet interaction by converting natural language instructions into browser actions, thereby automating tasks with high efficiency. It employs the latest technologies in LLMs (RAG, Chain of Thought, Few-Shot, etc.) to develop a performant system to generate Selenium code to pilot a browser. This enables the automation of various tasks, from form completions to bill payments.
The technology is built on open-source models and projects, which promote transparency and align with user interests. Furthermore, it supports local models that prioritize user privacy and control.
Consequently, we are searching for a world-class AI engineer intern to assist our team of AI engineers in constructing an AI product meant to have a huge impact.
The Role
As an AI Engineer Intern, your primary task will be to enhance LaVague's performance to generate actions from users' queries and improve our overall backend infrastructure.
Key Responsibilities
AI engineering stack: Your primary focus will be developing a robust and scalable application built on top of an LLM. This requires software development knowledge
Model Efficiency and Scalability: Implement strategies to increase the model's performance and scalability (RAG, few-shot learning, Chain of Thought, async requests to LLM server, automatic prompt optimization, etc.)
Performance Benchmarking: Regularly conduct performance benchmarking against key metrics to measure improvements. This involves setting up a robust framework for continuous testing and evaluation of the model.
Documentation and Reporting: Thoroughly document the development process, including the optimization techniques, results of various experiments, and insights gained. Prepare detailed reports and presentations to communicate findings and progress to the team.
Open Source Contribution and Engagement: Actively contribute to the development and release of features for this open-source project while also engaging with the online community in collaboration with the Community Team. This role involves ensuring that our developments are communicated transparently and contribute positively to the broader ecosystem
Stay Informed on AI Developments: Follow the latest trends and advancements in AI and NLP, especially regarding LLMs, to ensure LaVague incorporates the most effective and ethical practices.