Member of Technical Staff - Multimodal (Audio)

xAI•Palo Alto, CA

11d•$180,000 - $440,000•Onsite

About The Position

xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All engineers are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. The multimodal team at xAI creates magical AI experiences beyond text, enabling the understanding and generation of content across various modalities, including image, video, and audio. As a member of technical staff, you will drive the model’s multimodal capability through various aspects such as data, modeling, serving, and product. You will collaborate with pre-training, post-training, and product teams to push the frontiers of model capability as well as the end-to-end user experience.

Nice To Haves

Track record in leading studies that significantly improves the capability and performance of neural networks, whether through better data or better modeling.
Experience in data-driven experiment designs and systematic analysis for iterative model debugging.
Experience in developing or working with large-scale distributed machine learning systems.
Ability to do whatever is necessary to deliver the best end-to-end user experience.

Responsibilities

Creating and driving agendas to advance multimodal audio capabilities, which includes both audio understanding and audio generation.
Improving data quality, developing data filtering/generation techniques, and conducting data studies.
Creating evaluation frameworks and internal benchmarks.
Designing and implementing effective and efficient algorithms for achieving state-of-the-art audio model performance.