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# Human Data Manager

- **Company:** SPACEXAI LLC
- **Location:** Palo Alto, CA, United States
- **Salary:** $100,000.0 - $186,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Data Integrity, Python (Programming Language), DataOps, SQL Databases, Large Language Models, Data Strategy, Information Technology, Low-code, Data Management, Machine Learning Operations
- **Published:** August 11, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3351869633&tx=KL4747FFL&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity.

We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company's mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important.

All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates., * 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

## Description

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., * 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.


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