AI Data Engineer for Training & Evaluation

Socket.dev; Palo Alto (CA); On-site; USD 144,000 - 270,000; Full time

Benefits offered by this job

Equity

Comprehensive medical, vision, anddent

401(k) retirement plan

Short & long-term disability insurance

Life insurance

Employee discounts

Job summary

SpaceXAI is seeking a hands-on Engineer to ensure engineering teams get high-quality data for training and evaluation. You will craft data projects, partner with model teams, and drive data strategy at the intersection of engineering and data operations.

This role focuses on building pipelines, validating data, and improving annotation workflows to support rapid model development while maintaining strict data integrity and truthfulness.

Qualifications

• Bachelor's degree in engineering, CS, or related STEM or 4+ years of experience.
• Experience analyzing datasets to identify trends and quality issues.
• Experience collaborating with cross-functional teams.

Responsibilities

• Collaborate with model and engineering teams to define data projects.
• Own end-to-end delivery of data and eval projects.
• Build tools to measure data yield and eval lift.
• Maintain data integrity and validation processes.
• Research and evaluate data collection and annotation techniques.
• Shape model behavior through targeted data work.
• Design and improve annotation workflows and interfaces.
• Coordinate with Human Data Ops to scale projects and report blockers.

Skills

Cross-functional collaboration

Data analysis

Communication skills

Education

Bachelor's degree or equivalent experience

Tools

Python

SQL

Job description

SpaceXAI is seeking a hands-on Engineer to ensure engineering teams get high-quality data for training and evaluation. You will craft data projects, partner with model teams, and drive data strategy at the intersection of engineering and data operations.

This role focuses on building pipelines, validating data, and improving annotation workflows to support rapid model development while maintaining strict data integrity and truthfulness.