Member of Technical Staff - Reasoning

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 employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. ABOUT THE ROLE: As a Member of Technical Staff, you will build frameworks to improve the reasoning capability, build distributed reinforcement learning systems, techniques for inference time compute (e.g. tree search and planning), and develop environments for agents. You will get exposure and will be expected to solve and take ownership of components across the entire stack. RESPONSIBILITIES:

Build robust and scalable distributed RL systems. Optimise frameworks to enable complex inference-time reasoning. Develop environments and harnesses for agents.

BASIC QUALIFICATIONS:

Experienced with large-scale reinforcement learning systems. Designing and implementing distributed systems. Keeping up with state-of-the-art RL and inference time compute algorithms.

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:

Coding assessment in a language of your choice. Systems hands-on: Demonstrate practical skills in a live problem-solving session. Project deep-dive: Present your past exceptional work to a small audience. Meet and greet with the wider team.

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