Data Engineer

A venture-backed, early-stage AI and cybersecurity tech startup engineering breakthrough platforms to detect and neutralize sophisticated synthetic media, enterprise identity fraud, and advanced Deepfakes.

Backed by a substantial multi-million-dollar Seed round from leading venture capital funds, the company develops a multi-engine detection orchestrator combining over 10 proprietary AI/ML models with advanced cryptography and digital provenance verification (C2PA standard).

Operating with an elite founding team in a high-impact, agile environment, the company offers a unique early-stage opportunity to build core technology and foundational infrastructures from scratch.

Located in the Central District directly adjacent to primary train transit, operating on a flexible hybrid model with up to two days working from home.

Role Description-

• Serving as a Founding Data / ML Infrastructure Engineer, joining as the pivotal, sole owner responsible for designing, architecting, and scaling the entire company-wide data ecosystem.
• Reporting directly to the CTO, building robust, scalable data ingestion and processing pipelines handling high-throughput multimodal datasets (video, audio, and high-resolution imagery).
• Leading large-scale data scraping, automated harvesting, dynamic filtering, and end-to-end dataset curation across complex real-world threat vectors and detection scenarios.
• Developing production-grade Python frameworks for automated model benchmarking, dataset evaluation, performance telemetry, and reporting.
• Architecting enterprise-grade dataset versioning (e.g., DVC), storage optimization, cataloging, and metadata documentation to power continuous model training and validation loops.
• Collaborating closely with algorithmic researchers and core software squads to feed real-time detection engines with high-fidelity, validated training and evaluation data.

Requirements-

• 3 years of hands-on experience in Data Engineering or ML Data roles – Mandatory
• Deep, proven programming mastery in Python and strong hands-on experience utilizing multimedia processing libraries and tools (e.g., FFmpeg, OpenCV, Pillow, Librosa, or equivalent) – Mandatory
• Extensive, proven experience in large-scale data scraping, extraction, filtering, and systematic dataset curation – Mandatory
• Demonstrated track record managing structured dataset versioning, large-scale storage, and lifecycle management – Mandatory
• High degree of self-direction, intellectual autonomy, and comfort navigating shifting priorities within an agile, fast-paced early-stage startup environment – Mandatory
• Strong analytical mindset, proactive problem-solving abilities, and fluent professional communication in English (written and verbal) – Mandatory