Industrial Vision AI Engineer - Edge & MLOps

toyota; Georgetown (KY); On-site; USD 120,000 - 170,000; Full time

Job summary

Toyota's Manufacturing Innovation and Transformation Department is seeking a passionate Engineer to lead end-to-end engineering and deployment of next-generation automated quality inspection systems for Toyota's North American facilities. You will design computer vision models, optimize edge inference, and integrate vision with PLCs and industrial networks, collaborating across NAMCs, TTC, and IT teams.

The role demands strong ML skills and hands-on experience with Docker/Kubernetes in a

Qualifications

• Bachelor's degree or higher in Engineering, Computer Science, IT, or related field.
• 2+ years in industrial machine vision and edge AI deployment.
• 3+ years of AI/ML experience with camera-based vision systems.
• Experience managing the full model lifecycle: data collection, labeling, validation, rollout, monitoring, retraining.
• Hands-on experience with containerization (Docker) and orchestration (Kubernetes).
• Knowledge of ONNX Runtime, TensorRT, and embedded hardware optimization.
• Willingness to work weekends, holidays, shutdowns, and multiple shifts as needed; up to 30% overtime.
• Ability to travel up to 30% (US, Canada, Mexico, other manufacturing sites, and Japan).

Responsibilities

• Model Development & Training
• Design and implement computer vision models for defect detection, segmentation, and classification.
• Accelerate training cycles using synthetic data, active learning, and domain randomization, including for rare edge-case defects.
• Production Deployment & MLOps
• Package models with Docker and manage deployments via Kubernetes (K8s) or equivalent orchestration.
• Implement MLOps practices - version control, rollback strategies, and observability for latency, lighting drift, and false-positive/negative rates on production lines.
• Edge Optimization
• Optimize inference for edge/embedded hardware (e.g., NVIDIA Jetson, Intel accelerators) to meet real-time latency requirements for moving-line inspection.
• Maintain consistent performance across varying lighting, optics, and surface conditions.
• Manufacturing System Integration
• Integrate vision systems with PLCs, encoders, triggers, and industrial networks using OPC-UA, MQTT, and REST.
• Align deployments with Toyota's IC S+, GALC, and TVIP architecture standards for plant-level connectivity and reliability.
• Data Strategy & Quality Control
• Lead defect image collection campaigns, manage annotation workflows, and set quality gates for model validation.
• Use synthetic data pipelines and augmentation to improve robustness and reduce training time.
• Reliability & Sustainment
• Meet uptime/availability targets through proactive monitoring, calibration (MSA), and backup/restore processes.
• Implement drift detection, audit false negative risk, and conduct root cause analysis for inspection failures.
• Innovation & Collaboration
• Pioneer new deflectometry, reflection, and multi-camera imaging technologies to elevate vehicle surface quality control.
• Lead inspection booth software deployments from concept through SOP (Start of Production).
• Evaluate optical AI solutions through on-site plant trials, compiling data for North American rollout.
• Collaborate across NAMCs, Toyota Motor Corp (TMC), Toyota Technical Center (TTC), PE support shops, IT (One Tech), automation teams, and Enterprise AI divisions.

Skills

Industrial machine vision

Edge AI deployment

Model lifecycle management

Latency optimization

Education

Bachelor's degree or higher in Engineering/CS

Tools

Docker

Kubernetes

ONNX Runtime

TensorRT

Job description

Toyota's Manufacturing Innovation and Transformation Department is seeking a passionate Engineer to lead end-to-end engineering and deployment of next-generation automated quality inspection systems for Toyota's North American facilities. You will design computer vision models, optimize edge inference, and integrate vision with PLCs and industrial networks, collaborating across NAMCs, TTC, and IT teams.

The role demands strong ML skills and hands-on experience with Docker/Kubernetes in a