We are looking for research engineer with extremely strong technical experience in training Generative AI models. You’ll be part of the research team, helping build our core multimodal foundation models and manage training runs across thousands of GPUs.
Responsibilities
- Lead and contribute to cutting-edge research in multimodal foundation models
- Design, develop, and experiment with novel algorithms, architectures, and techniques that enhance the performance, efficiency, and scalability of our AI models.
- Optimize the performance of models for deployment in production environments, focusing on latency, throughput, and computational efficiency without compromising accuracy or robustness.
- Inspect and manage large-scale data clusters to find inefficiencies and bottlenecks in model training, and data loading
- Collaborate with cross-functional teams including data, applied research and infrastructure
Experience
- Very strong demonstrated engineering ability in Python and Pytorch.
- Experience building ML models from scratch in Pytorch.
- Academic or Professional experience with (and understanding of) generative multimodal models such as Diffusion Models and GANs, as well as deep learning concepts such as Transformers.
- Good to have familiarity with Linux clusters, systems & scripting.
- Good to have experience working with large distributed systems (>100 GPUs).
Your application are reviewed by real people.
