ML Data Infrastructure Engineer

Cognitiv is hiring an ML Data Infrastructure Engineer to build and evolve scalable data capabilities for its advertising platform. The role focuses on feature pipelines, embedding services, ad model training infrastructure, and improving system performance and reliability.

Responsibilities:

• High-Performance Feature Engineering & Infrastructure: Architect, build, and maintain low-latency, high-throughput feature pipelines — batch, real-time streaming, and point-in-time correct historical features — to power our real-time bidding systems
• Advanced Embeddings & Generative AI Integration: Leverage LLMs and deep learning models to extract rich contextual and user-level embeddings into the core feature store/serving system, optimizing embedding generation, indexing, and online retrieval for sub-millisecond serving SLAs
• Model Training Pipeline Optimization: Own and continuously enhance Cognitiv's ad model training pipelines, improving training speed, resource utilization, and throughput for large-scale deep learning models
• System Scalability, Reliability & Efficiency: Establish technical standards for monitoring, testing, and CI/CD across feature and training infrastructure to ensure robust system SLAs/SLOs
• Partner with Modeling & Data Science: Translate complex signals into production-ready features that directly boost model performance (e.g., CTR/CVR prediction)