ML Research Engineer - LLMs

→ UPDATE: Only applications submitted to https://dynamo.ai/apply-now will be considered.

Mission

At DynamoFL, we believe that LLMs must be developed with privacy, personalization, and real-world constraints in mind. Our ML team comes from a culture of academic research driven to democratize AI advancements responsibly. By operating at the intersection of ML research and industry applications, our team empowers Fortune 500 companies’ adoption of frontier research for their next generation of LLM products. Join us if you:

• Wish to work on the premier platform for private and personalized LLMs. We provide the fastest end to end solution to deploy research in the real world with our fast-paced team of ML Ph.D.’s and builders, free of Big Tech / academic bureaucracy and constraints.
• Are excited at the idea of democratizing state-of-the-art research (performance, compression, and privacy optimizations) for all companies and products, not just ads for Big Tech.
• Wish to work on a fast-paced team of builders
• Care about building a platform to empower fair, unbiased, and responsible development of LLMs and don’t accept the status quo of sacrificing user privacy for the sake of ML advancement.
• Are motivated to work at a rapidly expanding startup and see your impact on end customers in the timeframe of weeks not years. ‍

Responsibilities

• Own an LLM vertical with a specific domain and optimization focus (e.g. personalization, privacy, efficiency, explainability, or safety).
• Push the envelope by developing novel research that delivers real-world impact. Your research will directly empower our customers to more feasibly deploy safe and responsible LLMs.
• Co-author papers, patents, and presentations with our research team by integrating other members’ work with your vertical.

Expectations

• Deep domain knowledge in a specific LLM technique / area of research.
• Extensive experience in implementing multiple different types of LLM models and architectures in the real world. Comfortability with leading end-to-end projects.
• Adaptability and flexibility. In both the academic and startup world, a new finding in the community may necessitate an abrupt shift in focus. You must be able to learn, implement, and extend state-of-the-art research.