Human Data Engineer at SpaceXAI
Palo Alto, CA
$144,000–$270,000from the description
Aug 4, 2026
Palo Alto, CA
Sep 24, 2026
What this job asks for AI summary
This role sits at the boundary of data operations and AI/ML engineering, responsible for designing and delivering human data and evaluation projects that feed model training pipelines — specifically for Grok, SpaceXAI's large language model. Day-to-day work involves partnering with model teams to translate needs into data strategies, building tooling to measure data quality and eval lift, and maintaining rigorous data integrity across annotation workflows and multimodal datasets. Best suited for engineers with hands-on experience in LLM training data or evaluation.
Mid level · 4+ years · Full-time
Posted 3 times — it's one opening, so apply once.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
How this req sits in the market our data
Most people in this occupation already list these. Still required — just not what gets you shortlisted.
What the occupation pays Median $90,896 (middle half $69,863–$128,761). This posting is about at that midpoint.
Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Aug 5, 2026. It is a model, not a headcount.
Why we read it this way (6)
Location is not specified in the posting; the CBSA and state fields have been left blank. Travel to 'other SpaceXAI sites' is mentioned, suggesting a physical office exists, but no city or region is named.
The 4+ years of experience is stated as an alternative to a Bachelor's degree ('in lieu of a degree'), not as a standalone role-level requirement — total experience minimum is set to 4 as the closest proxy, but the role does not explicitly demand years of experience beyond that substitution clause.
SOC classification is genuinely ambiguous: the role blends data operations/analysis (15-2031) with ML/AI dataset curation and evaluation work closer to data science (15-2051). The primary day-to-day emphasis on data quality analysis, yield measurement, and eval reporting leans toward 15-2031, but the LLM training data and model evaluation focus is a meaningful signal toward 15-2051.
Most concrete technologies (Python, SQL, LLMs, annotation tooling, multimodal data) appear only under 'Preferred Skills and Experience' and are marked accordingly. The only hard-gated technical requirement is demonstrated experience analyzing datasets, which is stated in the Basic Qualifications.
The wide compensation range ($144,000–$270,000) likely spans multiple seniority levels; the posting does not specify a single level, and the title carries no seniority modifier.
Ignored 2 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): dataset analysis, annotation workflow design.
Read the full posting
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