... NVIDIA Jetson, Client accelerators) to meet strict real-time latency requirements for moving-line ... Required : • Bachelor's degree in Electrical Engineering, Mechanical Engineering, Computer ...
... NVIDIA Jetson, Client accelerators) to meet strict real-time latency requirements for moving-line ... Required : • Bachelor's degree in Electrical Engineering, Mechanical Engineering, Computer ...
Executive Nvidia Engineering information
What is the difference between Executive Nvidia Engineering vs Nvidia Hardware Engineer?
| Aspect | Executive Nvidia Engineering | Nvidia Hardware Engineer |
|---|---|---|
| Required Credentials | Bachelor's or Master's in Engineering, Business, or related fields; leadership experience | Bachelor's or Master's in Electrical, Computer, or Hardware Engineering; technical certifications |
| Work Environment | Leadership meetings, strategic planning, cross-department collaboration | Design, testing, and development of hardware components in labs or offices |
| Employer & Industry Usage | Used in corporate leadership, product strategy, and high-level project management within Nvidia | Used in R&D, product development, and technical implementation teams at Nvidia |
Executive Nvidia Engineering roles focus on strategic leadership, project oversight, and high-level decision-making, often requiring management experience. Nvidia Hardware Engineers concentrate on designing and testing hardware components, requiring technical expertise. Both roles are integral to Nvidia's success but differ significantly in responsibilities and work environment.
What are the key skills and qualifications needed to thrive as an Executive Nvidia Engineer, and why are they important?
To thrive as an Executive Nvidia Engineer, you need advanced expertise in computer engineering, deep learning, and GPU architecture, typically supported by a relevant engineering degree and extensive industry experience. Proficiency with programming languages like C++ and Python, experience with CUDA, and familiarity with AI frameworks such as TensorFlow or PyTorch are crucial, as are potential certifications in cloud or AI technologies. Leadership, strategic thinking, and strong communication skills are vital for driving innovation and leading high-performance teams. These skills and qualities ensure the effective development and deployment of cutting-edge technologies while aligning technical initiatives with organizational goals.
What does an Executive Nvidia Engineering professional do?
An Executive Nvidia Engineering professional typically leads engineering teams and oversees technical projects at Nvidia, focusing on innovative solutions in areas such as graphics processing, artificial intelligence, and high-performance computing. They are responsible for setting technical vision, managing large-scale engineering operations, and aligning projects with the company's business goals. Additionally, they collaborate closely with other executives, stakeholders, and partners to drive strategic initiatives and ensure product excellence. Their role requires deep technical knowledge, leadership skills, and the ability to operate in a fast-paced, cutting-edge technology environment.
How does an Executive Nvidia Engineering role typically collaborate with cross-functional teams within the company?
In an Executive Nvidia Engineering position, collaboration with cross-functional teams is central to driving innovation and meeting business objectives. Executives often work closely with product managers, software and hardware engineering teams, research scientists, and business development leaders to align technical projects with strategic goals. They facilitate communication between technical and non-technical stakeholders, ensuring that engineering efforts support product roadmaps and customer needs. This role frequently involves leading cross-departmental meetings, resolving technical challenges, and mentoring team leads to foster a culture of collaboration and high performance.
What are the most commonly searched types of Nvidia Engineering jobs in Kentucky? The most popular types of Nvidia Engineering jobs in Kentucky are:
What are popular job titles related to Executive Nvidia Engineering jobs in Kentucky? For Executive Nvidia Engineering jobs in Kentucky, the most frequently searched job titles are:
What job categories do people searching Executive Nvidia Engineering jobs in Kentucky look for? The top searched job categories for Executive Nvidia Engineering jobs in Kentucky are:
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Manufacturing Innovation Advanced Technology Engineer
Georgetown, KY • On-site
Full-time
Re-posted 21 days ago
Job description
Job Summary:
HireTalent is a staffing and recruiting firm seeking an Advanced Technology Engineer to develop and deploy AI-powered machine vision systems for defect detection and quality inspection in manufacturing. The role focuses on building production-ready computer vision models and integrating them into manufacturing systems while optimizing for real-time edge hardware.
Responsibilities:
• Design and implement computer vision models for defect detection, segmentation, and classification.
• Accelerate training cycles using synthetic data, active learning, and domain randomization to address rare defects and specification variance.
• Package models and services using Docker and manage deployments through Kubernetes or equivalent orchestration tools.
• Implement version control, rollback strategies, and monitoring for latency, model drift, and false-positive/false-negative metrics.
• Optimize inference for edge and embedded hardware (e.g., NVIDIA Jetson, Client accelerators) to meet strict real-time latency requirements for moving-line inspection.
• Ensure consistent performance under varying lighting, optics, and surface conditions.
• Integrate vision systems with PLCs, encoders, triggers, and industrial networks using OPC-UA, MQTT, and REST protocols.
• Align deployments with plant-level architecture and connectivity standards to ensure reliability and scalability.
• Lead data collection campaigns and manage annotation workflows.
• Establish quality gates for model validation.
• Utilize synthetic data pipelines and augmentation techniques to improve robustness and reduce training time.
• Ensure uptime and availability targets through proactive monitoring, calibration (MSA), and backup/restore processes.
• Implement drift detection, audit false-out risks, and perform root cause analysis for inspection failures.
• Develop and deploy production-grade machine learning models for industrial vision inspection systems.
• Accelerate model development using synthetic data and advanced AI techniques.
• Deliver containerized software optimized for edge hardware.
• Lead projects from concept through launch, including scheduling, milestone tracking, and cross-functional coordination.
• Evaluate new technologies in manufacturing environments and build business cases for adoption.
• Collaborate with internal engineering, IT, automation, and production teams to integrate robust AI solutions into high-volume manufacturing.
Qualifications:
Required:
• Bachelor’s degree in Electrical Engineering, Mechanical Engineering, Computer Science, IT, or related field.
• 5+ years of experience in industrial machine vision and edge AI deployment.
• Strong proficiency in Python and C++.
• Experience with ML frameworks (PyTorch, TensorFlow).
• Hands-on experience with Docker and Kubernetes.
• Familiarity with ONNX Runtime, TensorRT, and embedded optimization.
• Experience integrating vision systems with PLCs and industrial protocols (OPC-UA, MQTT).
• Experience managing the full AI lifecycle: data collection, labeling, validation, rollout, monitoring, and retraining.
• Knowledge of object detection, classification, and segmentation models.
• Experience with industrial cameras, lighting, and trigger-based image capture.
Preferred:
• Master’s degree or advanced engineering degree.
• Experience deploying automotive or high-volume production equipment.
• Robotics experience (operation, teaching, maintenance, safety).
• Expertise in synthetic data generation (GANs, VAEs, NeRFs, Blender) and domain randomization.
• Experience with high-speed inline inspection systems and IIoT data pipelines.
• Strong understanding of calibration, MSA, PFMEA, and quality-critical inspection requirements.
Company:
HireTalent is a certified Minority Business Enterprise (MBE) workforce solutions firm, specializing in securing the best talent fits in Executive/Retained Search, Direct Hire Placements, MSP, SOW, and nationwide hiring program management and support. Founded in 1997, the company is headquartered in New York, USA, with a team of 1001-5000 employees. The company is currently Late Stage.
HireTalent is a staffing and recruiting firm seeking an Advanced Technology Engineer to develop and deploy AI-powered machine vision systems for defect detection and quality inspection in manufacturing. The role focuses on building production-ready computer vision models and integrating them into manufacturing systems while optimizing for real-time edge hardware.
Responsibilities:
• Design and implement computer vision models for defect detection, segmentation, and classification.
• Accelerate training cycles using synthetic data, active learning, and domain randomization to address rare defects and specification variance.
• Package models and services using Docker and manage deployments through Kubernetes or equivalent orchestration tools.
• Implement version control, rollback strategies, and monitoring for latency, model drift, and false-positive/false-negative metrics.
• Optimize inference for edge and embedded hardware (e.g., NVIDIA Jetson, Client accelerators) to meet strict real-time latency requirements for moving-line inspection.
• Ensure consistent performance under varying lighting, optics, and surface conditions.
• Integrate vision systems with PLCs, encoders, triggers, and industrial networks using OPC-UA, MQTT, and REST protocols.
• Align deployments with plant-level architecture and connectivity standards to ensure reliability and scalability.
• Lead data collection campaigns and manage annotation workflows.
• Establish quality gates for model validation.
• Utilize synthetic data pipelines and augmentation techniques to improve robustness and reduce training time.
• Ensure uptime and availability targets through proactive monitoring, calibration (MSA), and backup/restore processes.
• Implement drift detection, audit false-out risks, and perform root cause analysis for inspection failures.
• Develop and deploy production-grade machine learning models for industrial vision inspection systems.
• Accelerate model development using synthetic data and advanced AI techniques.
• Deliver containerized software optimized for edge hardware.
• Lead projects from concept through launch, including scheduling, milestone tracking, and cross-functional coordination.
• Evaluate new technologies in manufacturing environments and build business cases for adoption.
• Collaborate with internal engineering, IT, automation, and production teams to integrate robust AI solutions into high-volume manufacturing.
Qualifications:
Required:
• Bachelor’s degree in Electrical Engineering, Mechanical Engineering, Computer Science, IT, or related field.
• 5+ years of experience in industrial machine vision and edge AI deployment.
• Strong proficiency in Python and C++.
• Experience with ML frameworks (PyTorch, TensorFlow).
• Hands-on experience with Docker and Kubernetes.
• Familiarity with ONNX Runtime, TensorRT, and embedded optimization.
• Experience integrating vision systems with PLCs and industrial protocols (OPC-UA, MQTT).
• Experience managing the full AI lifecycle: data collection, labeling, validation, rollout, monitoring, and retraining.
• Knowledge of object detection, classification, and segmentation models.
• Experience with industrial cameras, lighting, and trigger-based image capture.
Preferred:
• Master’s degree or advanced engineering degree.
• Experience deploying automotive or high-volume production equipment.
• Robotics experience (operation, teaching, maintenance, safety).
• Expertise in synthetic data generation (GANs, VAEs, NeRFs, Blender) and domain randomization.
• Experience with high-speed inline inspection systems and IIoT data pipelines.
• Strong understanding of calibration, MSA, PFMEA, and quality-critical inspection requirements.
Company:
HireTalent is a certified Minority Business Enterprise (MBE) workforce solutions firm, specializing in securing the best talent fits in Executive/Retained Search, Direct Hire Placements, MSP, SOW, and nationwide hiring program management and support. Founded in 1997, the company is headquartered in New York, USA, with a team of 1001-5000 employees. The company is currently Late Stage.