ML Research Engineer — Privacy
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.
- 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 an ML privacy vertical with a specific domain and optimization focus (e.g. differential privacy, privacy-enhancing algorithms, attacks, anonymization).
- 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:
- Deep domain knowledge in privacy-preserving ML.
- Practical experience in training Differentially Private models using frameworks like Opacus, OpenDP, etc.
- Extensive experience in implementing multiple different types of LLM models and architectures in the real world. Comfortability with leading end-to-end projects.
- Nice to have: domain knowledge in attacks against ML models (CV or LLMs).
- 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.