Software Engineer (associate or Experienced)

Boeing Boeing · Aerospace · Tukwila, WA

Software Engineer at Boeing supporting a production-grade computer vision system, focusing on system architecture, performance, and reliability. Responsibilities include designing, developing, testing, and maintaining non-embedded software, translating requirements, optimizing products, and implementing emerging technologies. Requires experience with C/C++/Python/Java/Ada and CI/CD pipelines on cloud platforms. Preferred experience includes GPU computing (CUDA/NVIDIA), AI environments, embedded systems, and DO-178 certification.

What you'd actually do

  1. Designs, develops, tests, and maintains non-embedded software throughout the end-to-end lifecycle that meets industry, customer, safety, and regulation standards.
  2. Reviews, analyzes, and translates customer requirements into initial design of software products.
  3. Develops, maintains, enhances and optimizes software products and functionalities for systems integrations.
  4. Develops, documents and maintains architectures, requirements, algorithms, interfaces and designs for software products.
  5. Debugs and resolves issues identified to ensure the reliability and efficiency of software products.

Skills

Required

  • Bachelor's Degree
  • Ability to obtain a U.S. Secret Security Clearance
  • 2+ years of experience in C, C++, Python, Java, or Ada
  • 2+ years of experience setting up and managing CI/CD pipelines on public cloud platforms (e.g., Amazon Web Services (AWS) or Azure)

Nice to have

  • Experience in GPU-accelerated computing using CUDA and NVIDIA platforms
  • Supports research and implementation of current and emerging technologies, tools, frameworks, and regulations in the engineering Artificial Intelligence environment and contributes to Artificial Design Practice(s)
  • Experience with embedded systems development, DO-178 certification, avionics data busses, FPGA development, and Unreal Engine
  • Experience in software development processes in compliance with established internal and industry standards, guidelines, and best practices in the development, testing, and deployment of software
  • Knowledge of the full software development life cycle (requirements, design, code, test and certification)

What the JD emphasized

  • production-grade computer vision system
  • GPU-accelerated computing using CUDA and NVIDIA platforms
  • engineering Artificial Intelligence environment

Other signals

  • computer vision system
  • GPU-accelerated computing
  • engineering Artificial Intelligence environment