• Architect, scale, and maintain high-throughput backend data pipelines handling real-time asset ingestion across microservices. • Optimize PostgreSQL database performance, pub/sub architectures, and distributed storage systems under heavy traffic loads. • Lead service migrations toward Go-heavy patterns while maintaining performant TypeScript backend services. • Collaborate cross-functionally with Hardware, Machine Learning, Web, Mobile, and Data Science teams to design and deploy new pipeline features. • Act as a technical domain expert by conducting code reviews, mentoring peer engineers, and driving engineering best practices across the pipeline ecosystem.

• Lead the architectural evolution and backend execution for Flock's investigative workflows. • Design and deploy high-performance orchestration backends that manage complex LLM tool-calling, function execution, and multi-step reasoning workflows. • Engineer high-throughput integration pipelines and secure connectors linking conversational interfaces directly to core data platform microservices. • Partner with Machine Learning and Data Engineering teams to bridge unstructured LLM outputs with structured internal APIs, maintaining strict schema validation and error handling.

• Lead low-level Qualcomm Android BSP bring-up, devicetree modifications, and pSIM framework integrations across next-generation camera platforms. • Architect and customize low-level bootloaders, including XBL and ABL implementations, to optimize device boot security and efficiency. • Develop and debug firmware for Application Digital Signal Processors, aligning ADSP architecture with system processing requirements. • Partner with electrical engineering and validation teams to define low-level hardware integration, build custom debug hooks, and expand automated testing. • Analyze system tombstones, kernel logs, and performance traces to eliminate hardware-software latency and memory bottlenecks.

Role Description We are currently searching for a Full-Stack Developer: - Build end-to-end products, taking full ownership from backend API architecture to frontend UI development. - Develop, integrate, and maintain production-grade machine learning pipelines (ML), with a specific focus on Natural Language Processing (NLP) applications. - Design and implement fast, reliable, and scalable model inference APIs. - Manage data processing workflows to ensure optimal performance of ML models in production. - Deploy applications using AWS or cloud-agnostic methodologies, managing containerization and continuous integration/continuous deployment. Qualifications - Strong expertise in building user interfaces with React and developing backend APIs using Python and FastAPI. - Proven expertise working with Python-based Machine Learning (ML) pipelines, especially Natural Language Processing (NLP). - Hands-on experience with AWS or cloud-agnostic deployment (e.g., ECS/Fargate, Docker). - Solid experience setting up and managing CI/CD pipelines. - Strong understanding of data processing, scalable architecture, and exposing model inference APIs. - Resilience, emotional intelligence, and a focus on agile delivery. - A deep understanding of the difference between "coding" and "engineering" (knowing how to productionize and scale an ML model, not just run it in a notebook). Requirements - Advanced Oral English. - Advanced Spanish. Benefits - Fully remote.