Staff Automation AI Engineer Manufacturing Engineering

At General Motors our product teams are redefining mobility. Through a human-centered design process we create vehicles and experiences that are designed not just to be seen but to be felt. Were turning todays impossible into tomorrows standard from breakthrough hardware and battery systems to intuitive design intelligent software and next-generation safety and entertainment features.

Every day our products move millions of people as we aim to make driving safer smarter and more connected shaping the future of transportation on a global scale..

The Role:

General Motors is seeking an experiencedAutomation AI Staff Engineerwith deepexpertisein manufacturing systems and production AI to drive the next generation of intelligent manufacturing this role you willbe responsible fordesigning building and deploying advanced AI/ML systems specifically tailored for manufacturing operations.You willleverageDomain-SpecificLoRAfine-tuning multi-modal AI models and enterprise-grade infrastructure to solve complex manufacturing challenges across GMs global production facilities. This role requires hands-on technical execution combined with the ability toarchitectscalable production-ready AI solutions that directlyimpactmanufacturing efficiency quality and innovation.

Success in this role requires a unique blend of AI/MLexpertise manufacturing domain knowledge and systems engineering excellence. You will work at the intersection ofcutting-edgeAI technology and real-world manufacturing operations collaborating with plant engineers quality teams robotics specialists and IT infrastructure teams todigitize Manufacturing.

What Youll Do:

• Design and implement Domain-SpecificLoRAfine-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 visioncontrols 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 andFastAPIbackend with async patterns and optimized request routing

• Implement high-performance database solutions using PostgreSQL withpgvectoroptimization 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

• OptimizeCUDA 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

• EstablishMLOpsbest practices including CI/CD pipelines model versioning A/B testing and performance monitoring for manufacturing AI deployments

Your Skills & Abilities (Required Qualifications):

• 10 years professional software engineering or machine learning engineering experience

• 10 years specialized experience developing AI/ML solutions for manufacturing automation including controls systems integration robotics applications and machine vision

• DeepexpertiseinLoRAfine-tuning including parameter-efficient training dynamic adapter loading and domain-specific model customization

• Strong programming skills in Python withproficiencyinPyTorch(preferred) or TensorFlow for model development and trainingandproficiencyin C# or C for production system development.

• Production experience with full-stack development: React/TypeScript with custom hookscomponentcomposition patterns and type-level programming

• Expertisein backend development usingFastAPIwith async/await patterns custom middleware dependency injection and optimized request routing

• Advanced database engineering skills with PostgreSQL including custom extensionspgvectoroptimization query performance tuning and distributed database architectures

• Hands-on experience with Kubernetes deployments service mesh implementation Docker multi-stage builds and automated scaling strategies

• Demonstrated ability to deploy enterprise-grade ML models with high reliability low latency and production monitoring

• Proven experience executing end-to-end automation projectsincluding:controls system programming(PLC HMI) machine vision system deployment (camera calibration illumination setupvision inspection programming) and robotics integration (programming calibration)

What Will Give You a Competitive Edge (Preferred Qualifications):

• Experience fine-tuning and adapting foundation models for manufacturing domains usingLoRAQLoRA and parameter-efficient techniques with ability to train custom models when pre-trained solutions are insufficient

• Deepexpertisein 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-CNNEfficientDet and custom architectures for defect detection and quality inspection

• Expertisein 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 withMLOpsbest 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 integrateseamlessly with existing systems

• Experience designing and deploying AI-driven automation solutions in industrial or manufacturing environments.

• Practical application of machine learning and large language models (LLMs) for equipment control diagnostics optimization or engineering workflows.

• Demonstrated ability to architect scalable automation platforms integrating PLCs robotics edge computing cloud services and enterprise data systems.

• Experience defining standards governance and best practices for AI-enabled automation to ensure safety reliability cybersecurity and long-term maintainability.

• Knowledge of GMs controls standards (Global Common Controls Hardware Design Standards and Global Common Controls Software Design Integration Standards