Software Engineer - X Data

xAI•Palo Alto, CA

44d•$125,000 - $400,000

About The Position

As a Data Engineer in the X product engineering team, you will play a key role in providing comprehensive data solutions that own or serve a wide range of stakeholders, including our end-users and internal teams such as Product engineering, Algorithm, Legal, Finance and Sales. Our work utilizes AI, distributed computing and hybrid storage technologies, but extends beyond purely data-centric solutions, encompassing non-data-related challenges, ultimately maximizing the potential of data for the benefit of our diverse user base.

Requirements

3+ years of professional software engineering experience, ideally in data engineering or distributed systems.
Hands-on expertise in Python, Rust, Scala, Go or Java, and data pipeline toolings and distributed systems.
Knowledge of realtime and batch data processing tools such as Spark/Kafka/Flink/SQL and various storage systems in RMDBs/NoSQL
Experience solving large scale problems and comfortable doing incremental quality work while building brand new systems to enable future quality improvements.
Proven records of interpreting product requirements into engineering implementation plans, and effectively communicating with different groups (AI, product, marketing/sales and engineering).

Responsibilities

Build and operate a distributed data platform that powers hundreds of realtime and batch pipelines processing billions of events per day.
Design, build, and operate production-grade realtime and batch pipelines that ingest, process, validate, and deliver data powering user-behavior insights and product decisions.
Create shared datasets, fact tables, and internal data products that let other teams analyze, debug, and improve product performance.
Prototype and build tooling that automates and accelerates internal data workflows — backfills, dashboards, report generation, and self-serve access to data.
Own data correctness end to end: validate with output invariants, denominator reconciliation, and independent recomputation, and lead root-cause investigations when key metrics move unexpectedly.
Move fluidly across query engines and frameworks (e.g., BigQuery, Trino, Clickhouse for analytics; Flink, Kafka, Spark/Scalding for streaming and batch), choosing the right tool and adapting quickly to new infrastructure and environments.
Partner across product and business teams to surface where data gaps exist and prioritize the highest-impact opportunities for new data acquisition and improvement.
Iterate quickly on feedback, shipping the smallest useful increment with a strong bias toward efficient, accurate, and reliable solutions.