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Applied AI Engineer

LinkedIn Aurora Greater Los Angeles, CA
Entry level Posted April 5, 2026 Job link
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Requirements
  • 3+ years experience (5+ preferred) building and shipping production AI systems, ideally agentic products
  • Experience as an early engineer at a VC-backed startup, taking systems from 0 to 1
  • Comfortable working autonomously in a fast-paced, high-intensity environment with high ownership
  • Track record building enterprise AI platforms that handle complex, physical-world data (video, IoT, sensor streams)
  • Bonus: experience in robotics, autonomous systems or industrial computer vision Tech stack TypeScript, Python, FastAPI, Agentic AI, OpenCV, postgres, React Native What you have done
  • Built and shipped agent systems in production with orchestration, tool use, state management and human-in-the-loop workflows
  • Worked with physical-world data at scale (RTSP video, time-series telemetry, vibration, thermal, PLC / OPC-UA) under real constraints (noise, drift, latency, alignment)
  • Built multimodal AI pipelines combining vision (detection, segmentation, action recognition) with structured operational data
  • Designed or contributed to ontology, knowledge graph or structured context systems grounded in real asset hierarchies and processes
  • Integrated AI systems with ERP, CMMS, WMS, historians or PLC layers and handled normalization and schema mapping
  • Shipped end-to-end systems from ingestion and model serving through backend services (Python, TypeScript) to frontend interfaces What you will build
  • Agent runtime and orchestration across Vision Quality, Predictive Maintenance and Operations Planning agents
  • Context assembly from ontology and memory , tool dispatch, approval gates, tracing and cost controls
  • Multimodal sensor pipelines : video processing, YOLO / segmentation, FFT feature extraction and cross-sensor correlation
  • Industrial ontology / knowledge graph mapping plants, assets, sensors, work orders, materials and maintenance history
  • Memory layer including trace storage, playbooks, asset templates and transferable failure pattern libraries
  • Operational decision surfaces : dashboards, alerting workflows with evidence, replanning tools and audit trails
  • Integration connectors across ERP, CMMS, WMS and PLC systems into a unified schema Who you are
  • Have built AI systems from 0 to production in real environments
  • Strong background in AI/ML applied to physical-world systems
  • Understand that the hardest problems are in data ingestion, normalization, temporal alignment and ground truth
  • Comfortable operating in a high-intensity, fast-moving environment with significant ownership
Preferred Skills
  • 3+ years experience (5+ preferred) building and shipping production AI systems, ideally agentic products
  • Bonus: experience in robotics, autonomous systems or industrial computer vision Tech stack TypeScript, Python, FastAPI, Agentic AI, OpenCV, postgres, React Native What you have done
  • Designed or contributed to ontology, knowledge graph or structured context systems grounded in real asset hierarchies and processes
  • Integrated AI systems with ERP, CMMS, WMS, historians or PLC layers and handled normalization and schema mapping
  • Shipped end-to-end systems from ingestion and model serving through backend services (Python, TypeScript) to frontend interfaces What you will build
  • Agent runtime and orchestration across Vision Quality, Predictive Maintenance and Operations Planning agents
  • Multimodal sensor pipelines : video processing, YOLO / segmentation, FFT feature extraction and cross-sensor correlation
  • Industrial ontology / knowledge graph mapping plants, assets, sensors, work orders, materials and maintenance history
  • Memory layer including trace storage, playbooks, asset templates and transferable failure pattern libraries
  • Strong background in AI/ML applied to physical-world systems