Member of Technical Staff, Video Generation - Agent, RL at XAI LONDON LTD

Palo Alto, California, United States -

Full Time

Start Date

Immediate

Expiry Date

13 Feb, 26

Salary

440000.0

Posted On

15 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, JAX, SGLang, Spark, Ray, Video Generation, Reinforcement Learning, Data Generation Techniques, Temporal Reasoning, Action Recognition, Long-Horizon Prediction, Controllable Generation, Model Debugging, Agentic Systems, Human Data, Synthetic Data

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. 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 focused on Video Generation - Agent, RL, you will pioneer AI agents that perceive, reason about, and generate video content. You will advance both video understanding (e.g., temporal reasoning, action recognition, long-horizon prediction) and video generation (e.g., controllable generation, long-video planning/generation) within agentic systems. Tech Stack Python JAX SGLang Spark Ray 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 Developing agentic planners for short- and long-horizon video generation Designing and collecting human/synthetic data; developing data generation techniques, e.g., captioning Building evals and reward models for video generation Studying training recipes for advancing video understanding/generation and agent training Ideal Experience Track record in leading studies that significantly improve 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 SFT, RL, evals, and human/synthetic data. Experience in agentic RL training and video models is considered an advantage. 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. California Consumer Privacy Act (CCPA) Notice

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Responsibilities

As a multimodal engineer, you will develop AI agents that perceive, reason about, and generate video content. You will work on advancing video understanding and generation within agentic systems.