Research Scientist Manager – AI
| Company | Meta |
|---|---|
| Location | Menlo Park, CA, USA, New York, NY, USA, Bellevue, WA, USA, Sunnyvale, CA, USA |
| Salary | $177000 – $251000 |
| Type | Full-Time |
| Degrees | PhD |
| Experience Level | Senior |
Requirements
- 5+ years of hands-on experience in computer vision and/or NLP, in the setting of both research and engineering development
- Experience and track of recording in landing large research and/or product impacts in a fast-paced environment
- 3+ years of hands-on supporting and leading teams of research scientists and software engineers
- Proven technical vision in the field of generative AI
- Experience and knowledge of model efficiency techniques (quantization, distillation, etc.)
- Experience with cross functional collaboration with product and platform teams, as well as non-engineering functions
- Demonstrated experience recruiting, building, structuring, leading technical organizations, including performance management
Responsibilities
- Drive efficiency gains on training, fine tuning, and deployment of diffusion based image/video generative models through novel techniques
- Drive end-to-end development of diffusion based image/video generative models, including data sourcing and curating, filtering, experiment design, evaluation and more
- Lead a team of applied researchers and software engineers to drive revenue for Meta
- Communicate, collaborate, and build relationships with clients and peer teams to facilitate cross-functional projects
- Remain up-to-date on ongoing research and software development activities in the team, help work through technical challenges, and be involved in design decisions
- Remain involved in the research community, both understanding trends, and setting them
Preferred Qualifications
- PhD in speech/language/vision deep learning, artificial intelligence, and/or related technical field
- Experience and knowledge of ML frameworks like PyTorch
- Experience and knowledge of large-scale data platforms
- Experience and knowledge of training diffusion based image/video/speech generative models, fine-tuning on datasets