xAI
Software Engineer - X Data
About this role
Join xAI's X product engineering team as a Data Engineer to design and operate a distributed data platform processing billions of events daily. You'll build production-grade pipelines, create shared datasets, and own data correctness end-to-end while partnering with product, algorithm, and business teams.
What you'll do
- Design, build, and operate production-grade realtime and batch data pipelines for ingestion, processing, validation, and delivery
- Create shared datasets, fact tables, and internal data products enabling analysis and product performance improvements
- Develop tooling to automate data workflows including backfills, dashboards, and self-serve data access
- Ensure data correctness through validation, reconciliation, and root-cause investigations of metric anomalies
- Select and adapt appropriate tools across query engines (BigQuery, Trino, Clickhouse) and processing frameworks (Flink, Kafka, Spark)
- Partner with cross-functional teams to identify data gaps and prioritize high-impact data acquisition opportunities
What they're looking for
- Python, Rust, Scala, Go, or Java
- Spark, Kafka, Flink, and SQL
- Realtime and batch data processing
- RDBMS and NoSQL storage systems
- Distributed systems architecture
- Data validation and quality assurance
- Cross-functional communication
- Large-scale system design
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 time you designed a data pipeline to handle billions of events—what tools did you choose and why?
- How would you approach ensuring data correctness and catching bugs in a high-volume pipeline?