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ConsultNet Technology Services and Solutions Linkedin · Posted 7d ago

Autonomous Driving Software Engineer

Lehi, Utah, United States

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Indexed description

Embedded Software Engineer IV – AI Applications

Salt Lake City Region (hybrid/onsite)

Target Start Date : ASAP

Contract-to-Hire (Direct Hire possible)

Pay Rate: $70.00-$85.00/hr ($150-$175K)


Overview

Our client, a leader and growing presence in the robotics and autonomous vehicles space, is seeking a senior Embedded Software Engineer to deploy and optimize AI capabilities on constrained, real-time hardware platforms supporting autonomous robotic systems. This role focuses on embedded performance, efficient compute and memory utilization, and reliable real-time operation, bridging AI model development and production-grade embedded deployment.


Responsibilities

  • Deploy and optimize AI/ML models on embedded and constrained hardware platforms.
  • Tune compute, memory, latency, and power consumption for real-time embedded operation.
  • Convert trained AI models into production-ready embedded libraries or hardware-optimized modules.
  • Integrate embedded AI components with robotics, firmware, and system software.
  • Optimize AI inference pipelines using hardware accelerators and ML runtimes.
  • Validate and monitor AI performance on embedded platforms to ensure robustness and stability.
  • Collaborate with cross-functional AI, robotics, and software teams across research and product environments.
  • Continuously improve embedded AI systems through performance analysis, optimization, and updates.


Requirements

  • Bachelor's degree in Computer Engineering, Electrical Engineering, Computer Science, or a related field.
  • 10+ years of embedded software engineering experience, including 2–5+ years deploying AI/ML on embedded systems.
  • Expert-level programming experience with C/C++ for embedded and real-time systems.
  • Experience with RTOS and multithreaded embedded applications.
  • Hands-on experience deploying ML inference using TensorRT, ONNX Runtime, OpenVINO, or TensorFlow Lite.
  • Strong understanding of embedded Linux, microcontrollers, and hardware accelerators.
  • Proven ability to optimize performance, memory, and power usage in constrained environments.
  • Strong analytical skills and ability to deliver reliable, production-grade embedded AI solutions.

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