Remote AI Engineer

Summary

Turn Block Talent is an AI and automation company focused on solving complex problems in heavy industries. They are seeking a Remote AI Engineer to build and ship production AI systems, particularly in environments that handle complex physical-world data.

Responsibilities

• 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
• 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

Skills

• 3+ years experience building and shipping production AI systems, ideally agentic products
• 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)
• 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
• 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
• Think in terms of production systems: reliability, failure modes, cost and human interaction
• Comfortable operating in a high-intensity, fast-moving environment with significant ownership
• Motivated to work on real-world industrial problems that require both depth and execution
• 5+ years experience 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

Qualifications

Must Haves

• 3+ years experience building and shipping production AI systems, ideally agentic products
• 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)
• 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
• 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
• Think in terms of production systems: reliability, failure modes, cost and human interaction
• Comfortable operating in a high-intensity, fast-moving environment with significant ownership
• Motivated to work on real-world industrial problems that require both depth and execution

Nice to Haves

• 5+ years experience 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

Benefits

• Equity pool for employees is double that of the industry standard, with $200k+ equity value from day one.