xAI
AI Frontier · AI lab
- HQ
- Palo Alto, US
- Founded
- 2023
- Size
- 1,000+
- Website
- x.ai
Products
xAI currently has 56 active AI-related job listings. The majority of these roles, 57%, are focused on data. Engineering is the top function for these positions, with the United States being the primary hiring country. Frequent tech tags include audio_speech, synthetic_data, and model_serving. Over the last 30 days, xAI has posted 11 new AI roles, representing a 267% increase compared to the previous 30-day period.
Currently tracking 48 active AI roles, up 103% versus the prior 4 weeks. Primary focus: Data · Engineering. Salary range $148k–$600k (avg $302k).
xAI currently has 73 active AI-related roles in our index. The most common open titles are: AI Tutor - Arabic, AI Tutor - Audio Editing, AI Tutor - Azerbaijani, AI Tutor - Bulgarian, AI Tutor - Catalan. Most positions are in Engineering.
xAI's active AI hiring is concentrated in: data (70%), application (8%), serving infrastructure (8%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
xAI is hiring AI talent in: United States (43 roles), United Kingdom (2 roles), Japan (1 role).
Job postings at xAI most frequently mention: Speech & Audio Processing, Linguistics, Language Modeling, Data Labeling, Dialog Systems.
In the past 30 days, xAI has posted 30 new AI-related roles.
AI Frontier · AI lab
| Title | Stage | AI score |
|---|---|---|
| Member of Technical Staff - RL Inference Engineer to optimize inference stack for RL workloads, analyze performance bottlenecks, and implement novel RL techniques. Requires experience in distributed systems, LLM inference, and programming languages like Python/C++/Rust with frameworks like PyTorch/Jax/CUDA. Preferred skills include quantization, numerics, and inference engines. | ServePost-train | 9 |
| ML Infrastructure Engineer xAI is seeking an ML Infrastructure Engineer to build and optimize the reliable, high-performance ML platform powering their recommendations. This role involves designing and scaling GPU compute infrastructure, training frameworks, and experimentation tools, developing data pipelines, and integrating large-scale data, training, and inference systems. The engineer will collaborate with ML teams to productionize models, ensure scalability, reliability, and efficiency, and work across the full stack. Requires 2+ years of industry experience with large-scale production environments, distributed systems, GPU infrastructure, and ML platforms, proficiency in Python and C++/Rust, and familiarity with ML frameworks like JAX/PyTorch and Linux/orchestration tools. | Serve | 8 |
| Software Engineer - Platform Infrastructure (Rust, C++) Software Engineer focused on building and optimizing large-scale distributed systems and infrastructure for AI training clusters, involving low-level systems programming and hardware/software co-design. | ServeData | 7 |
| Backend Engineer - API Backend Engineer responsible for building and owning the xAI API that serves models to developers worldwide. This includes end-to-end system ownership for high-throughput, low-latency, and highly available inference, covering model serving infrastructure, request routing, SDK development, rate limiting, observability, and scaling. Requires expertise in Rust or C++, distributed systems, and observability, with preferred experience in LLM inference engines, serving frameworks, and agent orchestration. | ServeAgent | 7 |
| Member of Technical Staff - Voice Product The role focuses on building and shipping voice features for the Grok product, involving backend engineering for scalable, low-latency voice infrastructure and model integrations. The goal is to drive performance, reliability, and quality of voice interactions at a global scale. | Serve | 7 |
| Member of Technical Staff - Compute Infrastructure The role involves building and optimizing large-scale GPU clusters and the platform layer for AI training and inference. Responsibilities include low-level CUDA kernel development, Linux kernel internals, custom orchestration, and performance debugging across the full stack to accelerate AI model development. | ServeAgent | 7 |