Interdisciplinary Sys Engineer, Ges Na Ops Engineering

Amazon Amazon · Big Tech · Bellevue, WA · Systems, Quality, & Security Engineering

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 within global fulfillment networks. The engineer will bridge AI/ML models with physical systems, leading the development and deployment of sensor-driven automation solutions and ensuring seamless integration across hardware, software, and control layers.

What you'd actually do

  1. Lead end-to-end deployment of computer vision-enabled automation systems across material handling environments, from concept through production rollout
  2. Design and develop integrated systems combining cameras, sensors, edge compute devices, and control interfaces to enable real-time monitoring and decision-making
  3. Bridge AI/ML models with physical systems by enabling reliable data capture, processing pipelines, and low-latency inference on industrial equipment
  4. Own hardware-software integration, including device selection, network configuration, edge processing, and connectivity to cloud or on-prem systems
  5. Work closely with scientists to productionize computer vision models, ensuring robustness, scalability, and performance in live operational environments

Skills

Required

  • 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

Nice to have

  • 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

What the JD emphasized

  • computer vision
  • edge computing
  • automation systems
  • industrial settings
  • AI/ML models
  • production-grade systems
  • hardware-software integration
  • low-latency inference
  • industrial equipment
  • productionize computer vision models
  • live operational environments
  • system validation strategies
  • controls systems
  • closed-loop automation
  • hardware and automation solutions
  • intelligent automation systems
  • computer vision and/or machine learning
  • Industrial control systems
  • complex problem solving
  • tight schedule environment
  • sensors (vision, depth, etc.)
  • edge compute devices
  • industrial environments
  • cameras, sensors, embedded/edge computing platforms, or IIoT systems
  • complex automated material handling equipment
  • high-speed manufacturing
  • hardware and software integrated
  • deep learning, machine learning and computer vision
  • LLMs in production on GPUs, Neuron, TPU or other AI acceleration hardware
  • hardware design and validation
  • end-to-end programs
  • hardware/software systems
  • robotics, mechatronics, or physical AI systems

Other signals

  • production-grade systems
  • scalable
  • real-time operational intelligence
  • industrial settings