About the job
Amazon is seeking an innovative, systems-oriented Computer Vision & Automation Engineer to help design and deploy next-generation intelligent automation solutions across global fulfillment networks. This role focuses on integrating computer vision, edge computing, and physical automation systems to enable real-time operational intelligence, improve equipment performance, and optimize process flow. The ideal candidate is a hands-on interdisciplinary engineer with expertise spanning hardware systems, embedded/edge computing, and automation environments, capable of bridging the gap between science (AI/ML models) and real-world deployment in industrial settings.
Responsibilities
Lead end-to-end deployment of computer vision-enabled automation systems across material handling environments, from concept through production rollout
Design and develop integrated systems combining cameras, sensors, edge compute devices, and control interfaces to enable real-time monitoring and decision-making
Bridge AI/ML models with physical systems by enabling reliable data capture, processing pipelines, and low-latency inference on industrial equipment
Own hardware-software integration, including device selection, network configuration, edge processing, and connectivity to cloud or on-prem systems
Work closely with scientists to productionize computer vision models, ensuring robustness, scalability, and performance in live operational environments
Develop and execute system validation strategies including test plans, field trials, and performance benchmarking under real-world conditions
Integrate with controls systems (e.g., PLCs, industrial protocols) to enable closed-loop automation and actionable system responses
Design for safety, privacy, and reliability, including implementation of safeguards such as data filtering, masking, and fail-safe system behavior
Collaborate with vendors and internal teams to prototype and scale custom hardware and automation solutions
Drive standardization of architectures, deployment patterns, and engineering best practices for intelligent automation systems
Artifact (research, schematics, specifications, prototypes, 3D Models, analysis, test plans, strategic narratives, etc.) and set the standard in organization for engineering excellence.
Able to communicate ideas effectively to achieve the right outcome for team and customer. Seek diverse perspectives, listen to feedback, and are willing to change direction if it creates a better outcome. Harmonize discordant views and lead the resolution of contentious issues (build consensus).
Travel up to 30% throughout North America, which can vary up to three weeks consecutive travel including weekends.
Qualifications
Minimum
3+ years of manufacturing equipment development experience, or Master's degree in computer science or electrical engineering
5+ years of hardware engineering experience
3+ years of systems engineering experience, or Bachelor's degree in engineering, technology, computer science, machine learning, robotics, operations research, statistics, mathematics or equivalent quantitative field
Experience with video and image processing and compression algorithms and standards, computer vision and/or machine learning
Experience with Industrial control systems, both hardware and software
Experience in complex problem solving, and working in a tight schedule environment
Experience working with and configuring sensors (vision, depth, etc.) and edge compute devices in industrial environments.
Hands-on experience with cameras, sensors, embedded/edge computing platforms, or IIoT systems
Preferred
Experience with complex automated material handling equipment, packaging technologies, and systems and high-speed manufacturing
Experience in creating products and services with hardware and software integrated
Experience building complex software systems, especially involving deep learning, machine learning and computer vision, that have been successfully delivered to customers
Experience in embedded wireless systems, or experience in developing and deploying LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
5+ years of hardware design and validation of components, subsystems and systems experience
Experience owning end-to-end programs to drive results
Master’s or PhD in mechanical, Industrial Engineering, Operations, or a related STEM field.
Experience developing and supporting hardware/software systems across the product life cycle
Background in robotics, mechatronics, or physical AI systems