This role focuses on building advanced generative models, emphasizing diffusion-based and unified architectures, in privacy-sensitive environments.
Responsibilities include designing and optimizing large-scale training systems, enhancing GPU performance, developing fault-tolerant and efficient training pipelines, and supporting multimodal and generation-understanding models.
Qualifications require experience in large-scale deep learning, distributed training, GPU optimization, and familiarity with diffusion models and privacy-preserving ML. Preferred skills include fault-tolerant systems, low-level performance work, and experience with production ML infrastructure.
Compensation ranges from $156,000 to $316,800 annually, with benefits like health insurance, 401(k), and paid time off. The company values diversity, inclusion, and innovation, fostering a collaborative environment to inspire creativity and technological advancement.