Compliant-Ready AI for the Enterprise. Backed by Y Combinator.
About the role
At Dynamo AI , we believe that LLMs must be developed with safety, privacy, and real-world responsibility 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:
What they're looking for
- Domain knowledge in LLM evaluation and data curation techniques
- Extensive experience in designing and implementing LLM benchmarking, extending previous methods. 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
- Preferred: past research or projects in benchmarking LLMs
More about this role
At Dynamo AI , we believe that LLMs must be developed with safety, privacy, and real-world responsibility 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 on safe and responsible AI.
• Are motivated to work at a 2023 CB Insights Top 100 AI Startup and see your impact on end customers in the timeframe of weeks not years.
• 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.
- Own LLM evaluation processes and methods with a focus on generating benchmarks...
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