- Esperienza
- Qualsiasi
- Stipendio
- Da 140.000 a 250.000 dollari all'anno
- Aperture
- 1
- Pubblicato
- 6 ore fa
- Modalità di lavoro
- Lavoro da casa
- Riprendere
- È necessario candidarsi
Descrizione del lavoro
About the Company
Our partner is a YC-backed startup revolutionizing the AI training data sector through an innovative marketplace model. Instead of a traditional labor marketplace, they provide infrastructure enabling data producers to convert their existing datasets into AI-compatible formats and sell directly to AI laboratories. This democratized approach unlocks access to high-quality, valuable data resources, fueling rapid team growth to meet increasing demand.
Job Opportunity
This role is the organization’s highest recruitment priority, tackling a complex research challenge. In a decentralized data marketplace, maintaining data quality at scale poses the chief obstacle to expansion. As a Research Engineer, you will develop automated systems that consistently ensure top-quality data delivery from suppliers to buyers.
Responsibilities
- Detect and analyze data quality problems such as inconsistencies, format errors, and ingestion difficulties.
- Conduct initial manual assessments of data to comprehensively understand failure patterns.
- Create automated quality validation systems leveraging rule-based logic combined with AI methodologies to handle ambiguous cases.
- Design hybrid review frameworks integrating automation with human oversight where appropriate.
- Iteratively refine verification techniques to adapt to evolving datasets and advancing AI tools.
Qualifications
- Highly technical aptitude with rapid learning capability in a dynamic, evolving field.
- Experience in AI or machine learning engineering, or software engineering within AI-centric companies specializing in data ingestion and processing.
- Strong analytical reasoning skills for diagnosing potential data quality issues from foundational principles.
- Comfortable independently managing open-ended, complex problems through all stages.
- Preferred: Experience handling noisy or unstructured data formats.