Backend Engineer

• Architect the OmniTag Engine: Design and build the high-throughput, low-latency backend systems that execute billion-scale inference across Ray/Spark, transforming raw sensor data into unified multimodal representations.
• Scale Multimodal Data Pipelines: Own the complete data journey - from ingestion, normalization, and preprocessing of heterogeneous modalities (image, video, LiDAR, audio) through encoding, indexing, and cached embedding storage. Ensure pipelines are robust, observable, and meet the SLOs expected by downstream ML teams.
• Evolve the Vector Search and Retrieval Engine: Enhance our in-house billion-scale vector search engine to power RAG-driven few-shot dataset creation. Optimize embedding storage, retrieval performance, and filtering across billions of examples to enable rapid interactive mining workflows.
• Own Data Quality and Observability: Build comprehensive monitoring, logging, and alerting for multimodal data preprocessing pipelines. Develop data validation frameworks that catch regressions in data alignment, normalization, or encoding quality—critical for maintaining model performance.
• Collaborate on Encoder-Decoder Adaptation: Work closely with ML engineers to support domain-specific fine-tuning workflows, model versioning, and A/B testing of new encoders and decoders. Ensure the backend infrastructure enables rapid experimentation with emerging open-source multimodal foundation models.
• Drive Production Reliability: Establish patterns for graceful degradation, fault tolerance, and cost optimization. Operate OmniTag as a mission-critical data platform serving the entire ML organization, with a focus on reliability, debuggability, and operational excellence.

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