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Research Scientist - LLM/Computer Vision



Software Engineering
Chantilly, VA, USA · Remote
Posted on Monday, July 24, 2023
Research Scientist - LLM/Computer Vision
Remote or In-Person
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At DynamoFL, we believe that AI 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 AI products. Join us if you:

  • Care about building AI responsibly and ethically and don’t accept the status quo of sacrificing user privacy for the sake of ML advancement.
  • Are excited at the idea of democratizing state-of-the-art research for all companies and products
  • Want to push the envelope of what’s possible and aren’t afraid of working on greenfield projects that improve upon the state-of-the-art.
  • Wish to work on a fast-paced team of ML Ph.D.’s and builders
  • Are motivated to work at a rapidly expanding startup and see your impact on end customers in the timeframe of weeks not years.
  • Own an FL vertical with a specific domain and optimization focus (e.g. personalization, privacy, efficiency, explainability, or fairness).
  • Collaborate with our engineering team to deliver real-world applications of your algorithms for our customers.
  • Co-author papers, patents, and presentations with our research team by integrating other members’ work with your vertical.

Although our main products revolve around federated, distributed, and privacy-centric learning, we don’t expect you to have extensive FL (federated learning) experience. We do expect:

  • Currently pursuing or completed PhD in Machine Learning (intern/full time)
  • Deep domain knowledge in a specific LLM or computer vision technique / area of research.
  • Extensive experience in implementing multiple different types of LLM or CV models and architectures in the real world. Comfort 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.