Member of Technical Staff, Multimodal Understanding (Visual / Audio) at XAI LONDON LTD

New York, New York, United States -

Full Time

Start Date

Immediate

Expiry Date

31 Mar, 26

Salary

440000.0

Posted On

31 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Multimodal Understanding, Visual Processing, Audio Processing, Data Quality Improvement, Data Filtering Techniques, Data Generation Techniques, Evaluation Frameworks, Algorithm Design, Python, Jax, Rust, Neural Networks, Distributed Machine Learning, Experiment Design, Model Debugging, User Experience

Industry

technology;Information and Internet

Description

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. Focus Creating and driving engineering agenda to toward superhuman multimodal capabilities, which include both multimodal understanding and multimodal generation, across different modalities including image, video and audio. Improving data quality, developing data filtering/generation techniques, and performing data study, on pretraining scale. Creating evaluation frameworks and internal benchmarks. Designing and implementing effective and efficient algorithms for achieving state-of-the-art model performance. Ideal Experience Hands-on experience on visual, audio or multimodal pretraining. Track record in leading engineering 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. Ability to do whatever is necessary to deliver the best end-to-end user experience. 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 (“intro call”) during which a member of our team will ask some basic questions. If you clear the initial , you will enter the main process, which consists of four technical interviews: One-on-one engineering discussion & coding interviews (three meetings total) Meet the Team: 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. Location The role is based in the New York area. Candidates are expected to be located near the New York City area or open to relocation. 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.

How To Apply:

Incase you would like to apply to this job directly from the source, please click here

Responsibilities

The role involves creating and driving the engineering agenda towards superhuman multimodal capabilities, focusing on understanding and generation across various modalities. Additionally, it includes improving data quality and developing evaluation frameworks.