Human Data - Engineer

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 engineering and data operations: partnering with model teams, designing projects that capture meaningful signals, and driving data yield and eval lift. This is a hands-on technical role for engineers who deeply understand training and evaluation 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 eval projects that capture meaningful training signals and support rapid model development.
• →Build tools and systems to measure data effectiveness (data yield, eval lift, usage), and run evaluations yourself to continuously improve quality.
• →Maintain rigorous data integrity and truthfulness, including validation processes for factual accuracy; prioritize quality over quantity.
• →Research, implement, and evaluate techniques for data collection, annotation, generation, and multi-modal integration.
• →Shape Grok’s behavior and domain performance through targeted data work.
• →Design and improve annotation workflows, labeling interfaces, and related tools with a focus on data quality and integrity.
• →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.

• Bachelor’s degree in engineering, computer science, or a related STEM discipline, or 4+ years of 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

• Master’s degree or higher in a relevant technical field
• 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 tools, labeling interfaces, or data workflows 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 conducting model or dataset evaluations focused on quality, truthfulness, or alignment with product goals
• Experience in a scripting language like Python
• Experience in SQL or other data analysis tools

• Weekend work may be required.
• Travel to other SpaceXSI sites may be required.