AI Researcher & Engineer - Multimodal (Audio)

Qualifications

• Track record in leading research 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

• Project deep-dive: Present your past exceptional work to a small audience

Benefits

• Annual Salary Range

• $180,000 - $440,000 USD

Responsibilities

• As a multimodal researcher/engineer, 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

• Creating and driving a research agenda 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

Full Description

About the Role

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 multimodal researcher/engineer, 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.

Focus

• Creating and driving a research agenda 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.

Ideal Experiences

• Track record in leading research 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.

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.

Tech Stack

• Python

• Jax

• Rust

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 research discussion & coding interviews (three meetings total)

• Project deep-dive: Present your past exceptional work 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