Database & Infrastructure Engineer (Full Stack)
Kled AI operates a human data marketplace where contributors upload photos, videos, and other data to earn income.
Funding history
About Kled AI
Kled AI sources large, licensable datasets from verified human contributors. Its mobile app enables users to upload photos, videos, and other content, complete tasks, and receive payouts through methods including Venmo, PayPal, and crypto. Kled also offers datasets and an enterprise portal for organizations to find, filter, and assemble AI-ready data using natural-language search. Its stated clients include leading AI laboratories, governments, and research institutions.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will make the data layer world-class by optimizing and scaling PostgreSQL infrastructure that processes millions of files per day and stores hundreds of millions of media records. You will design indexing, partitioning, and query strategies for large-scale media datasets, improve ingestion, enrichment, and retrieval performance, build internal tools, create dataset sample packs, automate exports and delivery pipelines, and work across backend, ML, and product teams.
Requirements
- Strong PostgreSQL expertise in indexing, partitioning, and performance tuning
- Experience working with large datasets, preferably 100M+ records
- Deep understanding of storage systems such as S3 or similar object storage
- Strong backend experience with TypeScript, Python, or similar
- Comfort building internal tooling and automation scripts
- Ability to move between database, backend, and infrastructure work
- Experience with data pipelines, ETL, or transformation layers
- Experience with vector databases such as pgvector, FAISS, or Pinecone
- Experience delivering structured datasets to enterprise customers
- DevOps experience with CI/CD and infrastructure automation
- Experience working with media-heavy systems
Responsibilities
- Optimize and scale PostgreSQL (Supabase) infrastructure
- Design indexing, partitioning, and query strategies for large-scale media datasets
- Improve performance across ingestion, enrichment, and retrieval pipelines
- Build internal tools for querying and auditing large datasets
- Create customer-ready dataset sample packs
- Design and automate dataset exports and delivery pipelines using S3, secure transfers, and custom formats
- Work across backend, ML, and product teams to support new features