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Manager Nvidia Robotics Jobs in Kentucky (NOW HIRING)

Manager Nvidia Robotics information

What is the difference between Manager Nvidia Robotics vs Robotics Engineer?

AspectManager Nvidia RoboticsRobotics Engineer
Required CredentialsBachelor's/Master's in Engineering, Management experienceBachelor's/Master's in Robotics, Mechanical, or Electrical Engineering
Work EnvironmentTeam leadership, project management, strategic planningDesign, develop, test robotic systems
Industry UsageOversees robotics projects in tech and manufacturingBuilds and codes robotic systems in R&D labs

The Manager Nvidia Robotics typically oversees robotics projects, requiring leadership and management skills, while Robotics Engineers focus on designing and developing robotic systems. Both roles are integral in the robotics industry but differ in responsibilities and daily tasks.

What are the typical challenges faced by a manager Nvidia Robotics, and how can new hires prepare for them?

Managers in Nvidia Robotics often encounter challenges related to overseeing multidisciplinary teams, integrating cutting-edge AI and hardware solutions, and managing fast-paced project cycles. New hires can prepare by developing strong project management skills, staying current with robotics and AI advancements, and fostering effective communication between hardware, software, and research teams. Being adaptable and proactive in problem-solving is crucial, as the field rapidly evolves and projects may shift priorities based on technological breakthroughs or business needs.

What are the key skills and qualifications needed to thrive as a manager Nvidia Robotics?

To thrive as a Manager Nvidia Robotics, you need a solid background in robotics, computer science or engineering, combined with proven leadership experience and a relevant degree. Familiarity with NVIDIA’s robotics platforms (such as Isaac SDK), AI frameworks, and project management tools is typically required, along with certifications in project management or engineering disciplines. Strong communication, problem-solving, and team leadership skills help drive cross-functional collaboration and innovation. These capabilities are crucial for successfully guiding technical teams, delivering complex robotics solutions, and maintaining a competitive edge in a fast-evolving industry.

What does a manager Nvidia Robotics do?

A Manager of Nvidia Robotics leads teams that develop, implement, and optimize robotics solutions using Nvidia's AI and GPU technologies. They oversee projects related to robotics hardware, software, and systems integration, ensuring products meet technical and business requirements. This role involves collaborating with engineers, researchers, and product managers to drive innovation and maintain project timelines. Additionally, the manager acts as a bridge between technical teams and upper management, helping to set strategic goals and allocate resources effectively.
What are the most commonly searched types of Nvidia Robotics jobs in Kentucky? The most popular types of Nvidia Robotics jobs in Kentucky are:
What are popular job titles related to Manager Nvidia Robotics jobs in Kentucky? For Manager Nvidia Robotics jobs in Kentucky, the most frequently searched job titles are:

Manufacturing Innovation Advanced Technology Engineer

TECKNOMIC LLC

Georgetown, KY • On-site

$65K - $84K/yr

Other

Posted 3 days ago

New


Job description

Please Read Before Applying

  • 5+ years in industrial machine vision and edge AI deployment
  • Proficiency in Python and C++ with ML frameworks (PyTorch, TensorFlow)
  • Experience integrating vision systems with PLCs and industrial protocols (OPC-UA, MQTT)
  • Hands-on Docker containerization and Kubernetes orchestration
  • Onsite in Georgetown, KY; able to travel internationally as needed

About the Role

A leading automotive manufacturer’s Manufacturing Innovation / Advanced Technology group is seeking an Advanced Technology Engineer to develop and deploy production-grade machine learning and computer vision models for industrial inspection across high-volume manufacturing lines. You will accelerate model development with synthetic data, deliver containerized software optimized for edge hardware, and integrate robust AI solutions into manufacturing systems to improve competitiveness.

Responsibilities

  • Design and implement computer vision models for defect detection, segmentation, and classification
  • Accelerate training cycles using synthetic data, active learning, and domain randomization
  • Package models/services with Docker and manage deployments through Kubernetes or equivalent orchestration
  • Implement version control, rollback, and observability for latency, drift, and false-positive/negative metrics
  • Optimize inference for edge/embedded hardware (e.g., NVIDIA Jetson, Intel accelerators) for real-time 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
  • Align deployments with plant-level connectivity and reliability standards
  • Lead data collection campaigns, manage annotation workflows, and establish quality gates for validation
  • Ensure uptime via proactive monitoring, calibration (MSA), drift detection, and root cause analysis
  • Lead and manage projects from concept to launch (schedules, punch lists, milestones)
  • Collaborate across manufacturing centers, corporate technical/R&D centers, IT, and automation teams

Required Qualifications

  • Bachelor’s degree in EE, ME, Computer Science, IT, or a related field
  • 5 years of experience in industrial machine vision and edge AI deployment
  • Python and C++ with strong knowledge of ML frameworks (PyTorch, TensorFlow)
  • Containerization (Docker) and orchestration (Kubernetes)
  • ONNX Runtime, TensorRT, and optimization for embedded hardware
  • Integrating vision systems with PLCs and industrial protocols (OPC-UA, MQTT)
  • Full model lifecycle: data collection, labeling, validation, rollout, monitoring, retraining
  • Object detection, classification, and segmentation (semantic/instance models)
  • Industrial cameras, lighting, optics, and trigger-based image capture
  • Balancing inspection accuracy with false positives vs. flow-out risk
  • Project management (scope, schedules, vendor/contractor management, status updates)
  • Ability to travel domestically and internationally (Canada, Mexico, Japan) as needed

Technical Skills

  • Python
  • C++
  • PyTorch
  • TensorFlow
  • Computer Vision (detection, segmentation, classification)
  • Edge AI (NVIDIA Jetson, Intel accelerators)
  • ONNX Runtime
  • TensorRT
  • Docker
  • Kubernetes
  • OPC-UA
  • MQTT
  • REST
  • PLC integration
  • Industrial cameras / optics
  • Synthetic data / domain randomization
  • MLOps

Preferred Qualifications

  • Master’s or advanced degree in engineering or a related field
  • Academic research experience in new technology
  • Project management involving internal and external parties (6+ months)
  • Equipment deployment including PFMEA and quality control plans
  • Deploying automotive production equipment
  • Robotics — operation, teaching, maintenance, and safety
  • Synthetic data generation (GANs, VAEs, NeRFs, Blender) and domain randomization
  • High-speed inline inspection and vision-based process control
  • IIoT data pipelines and messaging standards
  • Calibration, measurement system analysis (MSA), and quality-critical inspection

Must-Have Skills

  • Machine Vision
  • Edge AI
  • Computer Vision
  • Python
  • C++
  • PyTorch / TensorFlow
  • Docker / Kubernetes
  • PLC Integration
  • OPC-UA / MQTT
  • MLOps

Monster Skills List

  • Machine Vision
  • Computer Vision
  • Edge AI
  • Deep Learning
  • Machine Learning
  • Python
  • C++
  • PyTorch
  • TensorFlow
  • ONNX
  • TensorRT
  • Object Detection
  • Image Segmentation
  • Classification
  • Defect Detection
  • Docker
  • Kubernetes
  • NVIDIA Jetson
  • MLOps
  • Model Deployment
  • OPC-UA
  • MQTT
  • REST API
  • PLC
  • Industrial Automation
  • Industrial Cameras
  • Synthetic Data
  • Domain Randomization
  • GANs
  • MSA
  • IIoT