Level

xAI

Analytics Engineer - X

xAI is hiring an Analytics Engineer - X in Palo Alto, United States. It pays $180k-$440k a year and Level rates it ; you can apply on xAI.

Level's read on this job

  • On Level's scale, this role is Works on AI ●●●○: the daily work is on AI products, without building the model.
  • Tools and skills the posting cites include Python, SQL and Spark.
  • Level classifies it as a senior role.
  • Level lists 296 other open roles at xAI.

AI in this role

Build and maintain large-scale data pipelines and statistical frameworks to support quantitative analysis and AI model development.

pythonsqlsparkkafkaflinkhadoop
data-engineeringdistributed-computingpredictive-modelingdata-pipelines

SpaceX was founded under the belief that a future where humanity is out exploring the stars is fundamentally more exciting than one where we are not. Today, SpaceX is actively developing the technologies to make this possible, with the ultimate goal of making life multiplanetary and expanding consciousness to the stars.

SpaceXSI is building Super Intelligence to help humanity understand the universe. Our teams are building the full Super Intelligence stack: from our Colossus supercomputers, to Grok models, to our suite of applications, including Grok Bot, Grok Build, Grok Imagine, Grok Voice, and X. We're creating a powerful flywheel where more compute trains better models, better models power more useful products, and what we learn from people using those products shapes what we build next.

ABOUT THE ROLE:

We are seeking a skilled Analytics Engineer to build and maintain robust data systems that enable high-impact quantitative analysis and business decision-making. This role combines strong software engineering practices with expertise in large-scale data processing and advanced analytical methods to deliver reliable, scalable solutions across the organization. This is an opportunity to work on mission-critical systems that power quantitative decision-making at global scale.

RESPONSIBILITIES:

  • Design, implement, and optimize end-to-end data pipelines for processing high-volume datasets using tools such as Spark, Kafka, Flink, etc.
  • Develop quantitative models and statistical frameworks to support experimentation, forecasting, and performance measurement.
  • Build and maintain data infrastructure that ensures data quality, consistency, and accessibility for analytical workflows.
  • Collaborate with product engineering, product, and operations teams to translate business requirements into production-grade data systems and insights.
  • Conduct A/B tests, causal analysis, and performance evaluations to drive measurable improvements in key metrics.
  • Implement monitoring, alerting, and automation for data systems to support real-time decision support.
  • Mentor team members on best practices for scalable data engineering and quantitative problem-solving.

BASIC QUALIFICATIONS:

  • 4+ years of experience building production data pipelines and infrastructure at scale.
  • Strong proficiency in Python, SQL, and distributed computing frameworks (e.g., Spark, Flink, Hadoop).
  • Demonstrated expertise in statistical methods, predictive modeling, hypothesis testing, and experimental design.
  • Solid understanding of cloud services for data storage, processing, and orchestration.
  • Bachelor's or Master's degree in Computer Science, Statistics, Applied Mathematics, or related quantitative field.
  • Excellent problem-solving skills with a focus on delivering business impact through reliable systems

PREFERRED SKILLS AND EXPERIENCE:

  • Prior work in consumer technology, or social media domains.
  • Experience with real-time streaming systems and low-latency data processing.
  • Contributions to open-source data tools or publications on large-scale analytics systems.
  • Track record of reducing operational costs or improving system efficiency through data optimizations.
  • Have the ability to bridge engineering excellence with rigorous analytical approaches.

COMPENSATION AND BENEFITS:

$180,000 - $440,000 USD

Base salary is just one part of our total rewards package at SpaceXSI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short & long-term disability insurance, life insurance, and various other discounts and perks.

SpaceXSI is an equal opportunity employer. For details on data processing, view our Recruitment Privacy Notice.

How we rate this

Analytics Engineer - X at xAI rates 65 out of 100 for how much of the daily work is AI. That makes it Works on AI (AI Level 3 of 4). The level is about AI in the job, not seniority.

Classification

Works on AI. The daily work is on AI products, without building the model.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Data EngineeringDistributed ComputingPredictive ModelingData PipelinesPythonSQLSparkKafka

Questions you could be asked

  1. Tell me about a project where data engineering was part of your work. What did you do?
  2. Tell me about a project where distributed computing was part of your work. What did you do?
  3. Tell me about a project where predictive modeling was part of your work. What did you do?
  4. Tell me about a project where data pipelines was part of your work. What did you do?
  5. Walk me through how you've used Python in your day-to-day work.

Adapt your resume

  • List these exact terms on your resume: Data Engineering, Distributed Computing, Predictive Modeling, Data Pipelines, and Python. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Show where AI is part of your daily process, not a one-off project. This role expects it to be a running habit.

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