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Remote Embedded Ai Jobs in Arizona (NOW HIRING)

Mission Lane

Compliance Manager

Phoenix, AZ · On-site +1

$129K - $140K/yr

In this role, you'll be the embedded compliance authority across Mission Lane's fraud, collections ... You can hold your own in a conversation about AI-driven collections models or evolving contacting ...

Viasat, Inc.

Software Architect

Tempe, AZ · On-site +1

$205K - $307K/yr

Experience with application of AI tooling to aid the development life cycle * 5G Experience a plus ... building embedded systems software and with a Master's degree in Computer Science or Computer ...

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Remote Embedded Ai information

What is a Remote Embedded AI engineer?

A Remote Embedded AI engineer is a professional who develops and integrates artificial intelligence (AI) algorithms into embedded systems, such as IoT devices, sensors, or smart appliances, while working from a remote location. Their role involves optimizing AI models to run efficiently on hardware with limited resources, ensuring reliable performance and low power consumption. These engineers typically collaborate with cross-functional teams to deliver intelligent, connected products, leveraging skills in machine learning, software development, and embedded hardware. Working remotely allows them to contribute to global projects without being tied to a specific office location.

What is the difference between Remote Embedded Ai vs Remote Machine Learning Engineer?

AspectRemote Embedded AiRemote Machine Learning Engineer
Required CredentialsBachelor's or higher in Computer Science, Electrical Engineering, or related fields; experience with embedded systemsBachelor's or higher in Computer Science, Data Science, or related fields; strong programming and statistical skills
Work EnvironmentEmbedded hardware, IoT devices, real-time systemsCloud platforms, data centers, software development environments
Industry UsageConsumer electronics, automotive, industrial IoTTech companies, finance, healthcare, research
Common Search/ComparisonYesNo

Remote Embedded Ai professionals focus on developing AI algorithms for embedded hardware and real-time systems, often working with IoT devices and specialized hardware. In contrast, Remote Machine Learning Engineers primarily develop models in cloud environments for data analysis and prediction. While both roles require strong programming skills, Embedded Ai emphasizes hardware integration, whereas Machine Learning Engineers focus on scalable model deployment.

What are some common challenges faced by Remote Embedded AI Engineers, and how can they be overcome?

Remote Embedded AI Engineers often encounter challenges such as limited access to hardware for testing, asynchronous communication with distributed teams, and integrating AI models within resource-constrained embedded systems. Overcoming these challenges involves utilizing remote debugging tools, setting up robust simulation environments, and maintaining clear, regular communication with team members. Collaboration platforms and thorough documentation help ensure smooth coordination, while staying updated on best practices in embedded AI can address technical limitations.

What are the key skills and qualifications needed to thrive as a Remote Embedded AI Engineer, and why are they important?

To thrive as a Remote Embedded AI Engineer, you need expertise in embedded systems, machine learning algorithms, and programming languages like C/C++, Python, or TensorFlow Lite, often supported by a degree in computer engineering or related fields. Familiarity with real-time operating systems (RTOS), edge AI development platforms, and version control tools such as Git is typically required. Strong problem-solving skills, effective remote communication, and self-motivation help you excel in collaborative yet independent work environments. These competencies are crucial for building efficient, innovative AI solutions on hardware platforms while ensuring seamless teamwork across distributed teams.
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Remote Embedded Ai jobs near you

Senior Embedded Graphics Software Engineer

Quest Defense Systems & Solutions, Inc.

Phoenix, AZ • On-site, Remote

$123K - $161K/yr

Other

Medical, Dental, Life, Retirement

Posted 16 days ago

Job description

Join the elite team at QDSS, where our engineers are at the forefront of developing cutting-edge Aerospace, Defense, Space, and Medical Device products. Your work will make a real impact, contributing to the core of engineering solutions that drive technological advancements. 

Quest Defense is seeking a Senior Embedded Graphics Software Engineer to support the development, modernization, and sustainment of graphics software for advanced avionics display systems. This role will focus on graphics driver development, graphics middleware, rendering technologies, and performance optimization within safety-critical embedded environments.

The ideal candidate will bring deep experience developing graphics software on embedded platforms, including OpenGL-based solutions, graphics drivers, display technologies, and real-time operating systems. This individual will work closely with platform, systems, and hardware engineering teams to enhance graphics performance, support legacy display modernization efforts, and help advance next-generation avionics display capabilities.

Hybrid work in Phoenix, AZ is preferred; however, qualified remote candidates will be considered. U.S. Citizenship or U.S. Permanent Resident status is required.

Key Responsibilities:   

  • Design, develop, integrate, and maintain graphics drivers, graphics middleware, and embedded graphics software supporting avionics display systems.
  • Support the modernization and enhancement of legacy graphics software and display technologies for safety-critical aerospace applications.
  • Develop and optimize OpenGL-based graphics solutions for embedded and real-time operating environments.
  • Collaborate with platform, systems, and hardware engineering teams to ensure efficient interaction between graphics software, GPUs, processors, memory, and display hardware.
  • Analyze and optimize graphics performance across multi-core processor architectures, including CPU/GPU shared resource utilization.
  • Investigate and resolve software performance issues related to memory architecture, cache behavior, rendering performance, and system throughput.
  • Support software integration, verification, debugging, and testing activities throughout the development lifecycle.
  • Participate in software architecture, design reviews, technical trade studies, and graphics technology evaluations.
  • Develop technical documentation and engineering artifacts in support of software development and certification activities.
  • Mentor junior engineers and provide technical leadership in embedded graphics and graphics driver development.

Required Qualifications: 

  • Bachelor's degree in Computer Engineering, Computer Science, Electrical Engineering, or a related technical discipline.
  • 10+ years of experience developing software for embedded and/or real-time systems.
  • Strong experience developing embedded graphics software using OpenGL or similar graphics APIs.
  • Experience developing graphics drivers, display drivers, or GPU-related software
  • Experience working within DO-178B and/or DO-178C software development environments for safety-critical aerospace systems.
  • Strong programming experience in C/C++ within embedded and real-time operating system environments.
  • Experience working with Real-Time Operating Systems (RTOS) in embedded environments. (DEOS highly preferred)
  • Working knowledge of multi-core processor architectures and their impact on software performance.
  • U.S. Citizenship or Permanent Resident status.

Preferred Qualifications: 

  • Experience with DEOS RTOS.
  • Experience supporting avionics display systems or aerospace embedded products.
  • Experience with CPU/GPU shared memory architectures and graphics performance optimization.
  • Familiarity with FAA certification requirements and multicore certification guidance.
  • Experience supporting legacy platform modernization efforts.
  • Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field.
  • Experience in the adoption and/or integration of emerging technologies (e.g., generative AI, automation platforms, digital assistants) into day-to-day operations for continuous improvement.

The QDSS Advantage:  

At QDSS, our advantage is purpose-driven work, collaborative teams, and complex challenges that push boundaries and build lasting impact. You'll grow your career while contributing to mission-critical programs that demand excellence and shape the future. 

What You'll Find Here 

  • Work That Matters - Next-generation, safety- and mission-critical projects where your contributions have real-world impact. 
  • Growth That's Supported - Competitive compensation, employer-matched 401(k), certification assistance, and clear opportunities for advancement. 
  • A Culture That Works - A flexible, collaborative, and people-first environment where teamwork, innovation, and balance are valued. 

Benefits Include 

  • Competitive pay, comprehensive medical/dental/life and disability coverage, 401(k) with employer match, professional development support, and a flexible, friendly workplace.

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