About xAI

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.

About the Role

The Omni team at xAI creates magical AI experiences beyond text, enabling understanding and generation of content across various modalities, including image, video, and audio.

As a multimodal engineer with a focus on audio, you will drive the model’s audio generation and understanding capabilities, particularly in the context of video and broader media generation. This includes advancing audio for video, music generation, general audio synthesis, speech processing, audio understanding, audio representation / tokenizer, and so on. You will contribute across data curation, modeling, inference serving, and product integration, working on both pretraining and post-training while collaborating with product teams to push the frontiers of model capability and end-to-end user experience.

Tech Stack

Python
JAX
PyTorch
Rust

Location

The role is based in the Bay Area [San Francisco and Palo Alto]. Candidates are expected to be located near the Bay Area or open to relocation.

Focus

Creating and driving research agendas to advance multimodal audio capabilities, including audio generation for video, audio understanding, audio generation, music generation, and speech generation.
Improving data quality for audio and multimodal datasets, developing advanced annotation, filtering, augmentation, and synthetic data generation techniques, and conducting in-depth data studies.
Designing evaluation frameworks, metrics, and internal benchmarks tailored to audio.
Designing and implementing effective and efficient algorithms for achieving state-of-the-art model performance.

Ideal Experience

Track record in leading studies that significantly improves the capability and performance of neural networks, whether 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.
Experience in graphics engines and rendering techniques is considered an advantage.
Ability to do whatever is necessary to deliver the best end-to-end user experience.
Audio-related data processing and model training experience.

Interview Process

After submitting your application, the team reviews your CV and statement of exceptional work. If your application passes this stage, you will be invited to a 15 minute interview (“phone interview”) during which a member of our team will ask some basic questions. If you clear the initial phone interview, you will enter the main process, which consists of four technical interviews:

One-on-one discussion & coding interviews (three meetings total)
Project deep-dive: Present your past exceptional work and your vision with xAI to a small audience.

Every application is reviewed by a member of our technical team. All interviews will be conducted via Google Meet.

Annual Salary Range

$180,000 - $440,000 USD

Benefits

Base salary is just one part of our total rewards package at xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

xAI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.

Member of Technical Staff, Video Generation - Audio