Used Tools & Technologies
LLMRequired Skills & Competences
Tag name is followed by "@" symbol and proficiency level value.
About proficiency levels:
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
CI/CD @ 3
Communication @ 6
Experimentation @ 3
Observability @ 3
AI @ 3
Reinforcement Learning @ 3
Data Visualization @ 3
Data Pipelines @ 3
- 1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;
- 3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;
- 7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;
- 10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.
Details
xAI is a small, motivated engineering organization focused on building AI systems that can accurately understand the universe and aid humanity. The team values hands-on contribution, curiosity, strong communication, and engineering excellence.
Role overview
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. The role focuses on building infrastructure and tools that enable large-scale reinforcement learning (RL) research and training.
Typical problems you will deal with include the following:
- Designing an efficient and robust environment for agentic model capabilities to perform actions in.
- Adding new features to evaluation frameworks to ease researcher and engineer workflows and increase observability.
- Onboarding new open-source evaluation datasets into internal evaluation frameworks and tracking model performance.
- Standardizing preprocessing pipelines for datasets that require complex pre-processing for large-scale RL training.
- Creating data augmentation pipelines to produce additional training data.
Responsibilities
- Creating and maintaining frameworks for agent, data, and model evaluation tasks.
- Building environments for AI agents.
- Developing tools for automating common workflows.
- Improving alerts, metrics, and error handling on large-scale RL jobs.
- Refactoring existing agent, data, evaluation, and training frameworks for better modularity.
- Designing operational procedures and coding standards to streamline transition from small-scale experimentation to large-scale RL training.
- Writing unit tests and CI/CD frameworks to support rapid development cycles.
Requirements
- Experience building and maintaining frameworks used by many engineers.
- Experience building high-performance sandboxes, virtual machines, and simulations.
- Experience building full-stack applications for automating workflows and data visualization.
- Experience in rapid iteration of research-to-production cycles.
- Experience in test automation and CI/CD.
Compensation and benefits
- Base salary: $180,000 - $440,000 USD per year.
- Total rewards package 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, see the Recruitment Privacy Notice linked in the original posting.