Member of Technical Staff - RL Infrastructure

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.

xAI is seeking experienced software engineers to create robust data pipelines, comprehensive evaluations for benchmarking LLMs, and automation frameworks to increase the productivity of researchers and engineers.

Typical problems you will deal with include the following:

1. We have a new agentic model capability that we’d like to improve. How do we design an efficient and robust environment for the agent to perform actions in?
2. Evaluations and observability are a core part of knowing what we need to improve in our models. What new features can we add into our evaluation framework to ease the workflow of researchers & engineers and increase observability?
3. A new open-source evaluation dataset has been released and researchers would like to track our models performance on it. How should we onboard it into our internal evaluation framework?
4. Datasets have been collected that require complex pre-processing to prepare it for large-scale RL training. How do we standardize our preprocessing pipelines to minimize dataset onboarding time?
5. A researcher on the team has an idea for how to augment a dataset to produce additional training data. How should we go about creating the data augmentation pipeline?

• →Creating and maintaining frameworks for agent, data, and model evaluation tasks.
• →Building environments for AI agents.
• →Tools for automating common workflows.
• →Improving alerts, metrics and error handling on large scale RL jobs.
• →Refactoring existing agent, data, eval, training frameworks for better modularity.
• →Designing operation procedures and coding standards to streamline the transition from small scale experimentation to large scale RL training. 
• →Writing unit tests, CI/CD frameworks to support rapid development cycles.

• Experience building and maintaining frameworks that are used by many engineers.
• Experience in building high-performance sandboxes, virtual machines, and simulations.
• Experience building full-stack apps for automating workflows and data visualization.
• Experience in rapid iteration of research to production cycles.
• Experience in test automation, CI/CD.