xAI
Analytics Engineer - X
About this role
SpaceXAI seeks an Analytics Engineer to design and optimize large-scale data pipelines and infrastructure that power quantitative analysis and business decision-making across the organization. You'll combine software engineering rigor with statistical expertise to build production-grade systems supporting experimentation, forecasting, and real-time insights.
What you'll do
- Design and optimize end-to-end data pipelines for high-volume datasets using distributed computing frameworks like Spark, Kafka, and Flink
- Develop quantitative models and statistical frameworks for experimentation, forecasting, and performance measurement
- Build and maintain data infrastructure ensuring quality, consistency, and accessibility for analytical workflows
- Conduct A/B tests, causal analysis, and performance evaluations to drive measurable metric improvements
- Implement monitoring, alerting, and automation for data systems to support real-time decision support
- Mentor team members on scalable data engineering and quantitative problem-solving best practices
What they're looking for
- Python and SQL
- Distributed computing frameworks (Spark, Flink, Hadoop)
- Statistical methods and hypothesis testing
- Predictive modeling and experimental design
- Cloud data services and orchestration
- Real-time streaming systems
- Data pipeline architecture
- Problem-solving and system optimization
Benefits
- Equity compensation
- Comprehensive medical, vision, and dental coverage
- 401(k) retirement plan
- Short and long-term disability insurance
- Life insurance
- Various discounts and perks
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xAI
xAI builds advanced AI infrastructure and systems, including the Grok model inference platform and Colossus GPU cluster. The company is hiring Mechanical, Electrical, and Facilities Engineers to design and maintain its data center operations, as well as Software Engineers to optimize high-performance inference systems and datacenter networking.
View all jobs at xAILikely interview questions
- Walk us through a complex data pipeline you built at scale—what were the key challenges and how did you optimize for performance?
- Describe your approach to designing an experiment and how you'd handle confounding variables in a causal analysis.