ML Data Engineer
Tasks
- Build evaluation dataset construction pipelines with integrity controls
- Build high throughput data loading systems for accelerator training
- Build ingestion systems for multimodal data
- Collaborate with ML researchers to align data with model needs
- Design and operate large scale data pipelines for AI training and evaluation
- Design storage architectures for cost throughput and latency
- Develop dataset versioning lineage and provenance tracking
- Document data systems schemas and operational procedures
- Drive observability of data quality drift and pipeline health
- Implement data cleaning deduplication filtering and quality assurance
- Implement data privacy redaction and consent enforcement
- Implement labeling workflows active learning and human in the loop improvement
- Optimize cost and performance with compression formats and caching
Perks/Benefits
- N/A
Skills/Tech-stack
Active Learning | Apache Beam | CI/CD | Code review | Data Integrity | Data Lineage | Data Modeling | Data Privacy | Data Quality | Data loading | Data redaction | Dataset versioning | Distributed Systems | High Throughput | High Throughput Data Loading | High-throughput data | Human-in-the-loop | Java | Multimodal Data | Python | Ray | Spark | Storage Architecture | Testing | The Loop
Education
Roles
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