Top Tech Jobs & Startup Jobs

Build, own, and improve the evals platform that measures model capabilities and behaviors. Design datasets and grading schemes, manage execution infrastructure, diagnose failures, and deliver robust tools engineers use to clean data, provide experiment feedback, and track model growth.

Build and operate the observability platform: design scalable metrics/logs/tracing infrastructure, implement high-performance telemetry pipelines, develop APIs/query engines/UIs, enforce instrumentation and alerting best practices, and own system reliability and performance.

Build and scale GPU compute infrastructure, training frameworks, and data pipelines. Integrate large-scale training and inference systems, productionize models with ML teams, ensure scalability and reliability, troubleshoot full-stack ML systems, and mentor junior engineers.

You will work on critical product challenges and contribute directly to xAI's mission, thriving in a meritocratic and hands-on environment.

Design and implement large-scale distributed systems for a supercomputing cluster, optimize low-level performance across GPUs, kernel, networking, and filesystems, collaborate on hardware-software co-design, maintain scalable reliable codebase, and build productivity tools.

The Document Control Specialist will manage technical documentation for engineering and construction projects, ensuring accuracy and compliance, while improving document control systems and workflows.

Seeking an exceptional Android engineer to develop an AI application. Responsibilities include designing UIs, optimizing performance, and integrating APIs for seamless user experiences.

Lead implementation of ETL patterns and reliable data infrastructure, partner with business and engineering teams to prioritize and deliver integrations, APIs, and reusable components, and support operational data applications in an Agile environment.

Design and run human data and evaluation projects to supply high-quality training signals for models. Partner with model and engineering teams, maintain data integrity, optimize annotation workflows, measure data impact using agents and tools, and scale processes across stakeholders.

Build and maintain scalable end-to-end data pipelines and infrastructure, enabling high-volume processing and reliable analytics. Develop quantitative models, run A/B tests and causal analyses, collaborate with product and operations to translate requirements into production-grade systems, implement monitoring/automation for real-time decision support, and mentor peers on data engineering and analytical best practices.