Internship Machine Vision Engineer Jobs in Lithonia, GA

We are seeking an experienced AI/ML Engineer with deep expertise in manufacturing systems and production AI to drive the next generation of intelligent manufacturing solutions. In this role, you will be responsible for designing, building, and deploying advanced AI/ML systems specifically tailored for manufacturing operations. You will leverage Domain-Specific LoRA fine-tuning, multi-modal AI models, and enterprise-grade infrastructure to solve complex manufacturing challenges across the company's global production facilities. This role requires hands-on technical execution combined with the ability to architect scalable, production-ready AI solutions that directly impact manufacturing efficiency, quality, and innovation.

Success in this role requires a unique blend of AI/ML expertise, manufacturing domain knowledge, and systems engineering excellence. You will work at the intersection of cutting-edge AI technology and real-world manufacturing operations, collaborating with plant engineers, quality teams, robotics specialists, and IT infrastructure teams to digitize Manufacturing.

What You'll Do

• Design and implement Domain-Specific LoRA fine-tuning architectures for manufacturing AI applications, including quality inspection, predictive maintenance, process optimization, and robotics control.
• Develop and deploy multi-modal AI systems that integrate machine vision, controls system, robotics controls, and production telemetry for real-time manufacturing intelligence.
• Build production-grade ML infrastructure supporting model training, fine-tuning, deployment, and monitoring across distributed manufacturing environments.
• Architect and develop full-stack AI applications using React/TypeScript frontend with custom state management and FastAPI backend with async patterns and optimized request routing.
• Implement high-performance database solutions using PostgreSQL with pgvector optimization for embedding storage, query performance tuning, and distributed architectures.
• Deploy and orchestrate AI services using Kubernetes with service mesh implementation, automated scaling strategies, and comprehensive monitoring and alerting.
• Develop proprietary embedding models optimized for manufacturing domain knowledge, including defect patterns, assembly sequences, and process parameters
• Optimize CUDA kernels and implement parameter-efficient training techniques for large-scale model fine-tuning on manufacturing datasets
  Design and build machine vision systems for automated quality inspection, defect detection, and process verification
• Train and deploy supervised learning models for robotics control and manufacturing execution system integration, enabling autonomous operations across production lines.
• Collaborate with cross-functional teams including manufacturing engineers, quality specialists, robotics teams, and plant operations to deliver measurable improvements in production metrics.
• Establish MLOps best practices including CI/CD pipelines, model versioning, A/B testing, and performance monitoring for manufacturing AI deployments.

Required Qualifications

• Masters's or higher degree in Computer Science, Electrical Engineering, or equivalent major or equivalent experience.
• 5+ years professional software engineering or machine learning engineering experience.
• 3+ years specialized experience developing AI/ML solutions for manufacturing automation, including controls systems integration, robotics applications, and machine vision.
• Deep expertise in LoRA fine-tuning, including parameter-efficient training, dynamic adapter loading, and domain-specific model customization.
• Strong programming skills in Python with proficiency in PyTorch (preferred) or TensorFlow for model development and training and proficiency in C# or C++ for production system development.
• Production experience with full-stack development: React/TypeScript with custom hooks, component composition patterns, and type-level programming.
• Expertise in backend development using FastAPI with async/await patterns, custom middleware, dependency injection, and optimized request routing.
• Advanced database engineering skills with PostgreSQL including custom extensions, pgvector optimization, query performance tuning, and distributed database architectures.
• Hands-on experience with Kubernetes deployments, service mesh implementation, Docker multi-stage builds, and automated scaling strategies.
• Knowledge of the company's controls standards (Global Common Controls Hardware Design Standards and Global Common Controls Software Design Integration Standards.
• Demonstrated ability to deploy enterprise-grade ML models with high reliability, low latency, and production monitoring.
• Proven experience executing end-to-end automation projects including: controls system programming (PLC, HMI), machine vision system deployment (camera calibration, illumination setup, vision inspection programming), and robotics integration (programming, calibration).

Preferred Qualifications

• Experience fine-tuning and adapting foundation models for manufacturing domains using LoRA, QLoRA, and parameter-efficient techniques, with ability to train custom models when pre-trained solutions are insufficient
• Deep expertise in multi-modal transformer architectures integrating vision encoders, sensor fusion layers, and control signal embeddings for end-to-end manufacturing AI systems
• Experience building custom training loops with gradient accumulation, mixed-precision training, dynamic loss scaling, and advanced optimization strategies (AdamW, LAMB, Lion)
• Deep knowledge of computer vision architectures beyond standard CNNs: Vision Transformers (ViT), DETR, Mask R-CNN, EfficientDet, and custom architectures for defect detection and quality inspection
• Expertise in self-supervised learning, contrastive learning (SimCLR, MoCo), and few-shot learning techniques for scenarios with limited labeled manufacturing data
• CUDA optimization experience for inference acceleration, including custom operator implementation, kernel fusion, and memory-efficient processing pipelines
• Advanced model compression techniques including quantization-aware training, pruning, knowledge distillation, and neural architecture search for manufacturing-specific constraints
• Experience with MLOps best practices including model drift detection, automated retraining pipelines, A/B testing frameworks, shadow deployments, and production monitoring at scale
• Strong collaborative skills working with manufacturing engineers and plant teams to translate production challenges into ML problem statements and deliver solutions that integrate seamlessly with existing systems
• Strong experience with machine vision systems, including image processing, defect detection, object recognition, and quality inspection algorithms.