Human Data Manager
xAI is hiring a Human Data Manager in Palo Alto, United States. It pays $100k-$186k a year and Level rates it ; you can apply on xAI.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- The posting mentions AI agents, No Code and Low Code among its tools and skills.
- Level classes it as a senior role.
- xAI has 296 other open roles on Level.
AI in this role
Manage human data projects, evaluation strategies, and annotation workflows to improve Grok model behavior and performance.
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:
You will ensure engineering teams get the right data they need—high-quality tasks and evaluations—by crafting and executing high-value Human Data projects. You sit at the intersection of model teams and data operations: partnering with engineering, designing projects that capture meaningful signals, and driving measurement of data impact. This is a hands-on role for people who understand training and evaluation deeply and want to influence data strategy.
RESPONSIBILITIES:
- Partner with model and engineering teams to understand needs and translate them into high-value data projects and evaluation strategies.
- Own end-to-end delivery of critical data and evaluation projects that capture meaningful training signals and support rapid model development.
- Leverage AI agents and existing platforms to measure data effectiveness and quantify impact (data yield, eval lift, usage).
- Maintain rigorous data integrity and truthfulness, including validation processes for factual accuracy; prioritize quality over quantity.
- Research and apply techniques for data collection, annotation, generation, and multi-modal integration.
- Shape Grok’s behavior and domain performance through targeted data work; improve annotation workflows using agents and no/low-code approaches where helpful.
- Manage plans that shape model behavior via data management, optimization, and analysis, including resources and timelines.
- Act as a liaison between engineering, technical staff, and tutoring / Human Data teams to drive alignment and knowledge sharing.
- Collaborate with Human Data Ops and stakeholders to scale projects, share learnings, and contribute to demand forecasting; report status, insights, and blockers for rapid decisions.
BASIC QUALIFICATIONS:
- Bachelor’s degree, or 4+ years of relevant experience in lieu of a degree
- Experience collaborating with cross-functional teams (engineering, research, product, or annotation/operations groups)
- Demonstrated experience analyzing datasets to identify trends, anomalies, quality issues, or integrity problems
PREFERRED SKILLS AND EXPERIENCE:
- Degree in engineering, computer science, data analysis, or a related STEM discipline (Bachelor’s or higher)
- Direct experience curating, evaluating, or improving training or evaluation datasets for large language models or other AI/ML systems
- Experience designing, supporting, or optimizing annotation workflows or data processes that prioritize factual accuracy and data integrity
- Familiarity with multimodal data (text + images, code, or other modalities) or domain-specific data (science, mathematics, programming, recent events, etc.)
- Experience with model or dataset evaluations focused on quality, truthfulness, or alignment with product goals
- Comfort using AI agents, no/low-code tools, or light scripting (e.g., Python) to prototype workflows and measurement
- Experience with SQL or other data analysis tools
ADDITIONAL REQUIREMENTS:
- Weekend work may be required.
- Travel to other SpaceXSI sites may be required.
COMPENSATION AND BENEFITS:
$100,000 - $186,000 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
Human Data Manager at xAI rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ 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
Questions you could be asked
- How do you decide when an AI agent can act on its own versus asking for approval first?
- Tell me about a project where data management was part of your work. What did you do?
- Tell me about a project where annotation was part of your work. What did you do?
- Tell me about a project where model evaluation was part of your work. What did you do?
- Tell me about a project where data operations was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: AI agents, Data Management, Annotation, Model Evaluation, and Data Operations. 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.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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