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Applied Intuition

Robot type
Autonomous Vehicle · Robot AI and Software
Location
SunnyvaleCaliforniaUSA
Job type
Artificial Intelligence
Posted
Apr 8, 2026
Salary
$183,000–$253,000 a year
Full-time

Embedded AI Engineer – Android Automotive (On-Device Intelligence)

Job description

Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.;

Job responsibilities

  • Deploy and run production-grade ML inference and learning systems on Android Automotive (AAOS)
  • Implement on-device multimodal LLMs, including schema design and safe dispatch to local vehicle APIs
  • Integrate models using TensorFlow Lite, ONNX Runtime, or specialized vendor SDKs
  • Profile and optimize models for strict latency, memory, power, and thermal budgets
  • Instrument runtime performance across CPU, GPU, and NPU acceleration layers
  • Design safety boundaries and guardrails for model outputs, including tool-call allowlists and fallback logic
  • Interface directly with vehicle signals, sensors, and system services using C++ and JNI

Job requirements

  • BS, MS, or PhD in Computer Science, Electrical Engineering, or a related technical field
  • 3+ years of experience shipping ML inference on embedded, mobile, or automotive platforms
  • Strong proficiency in C++ and experience with native Android integration (JNI)
  • Expertise in model optimization techniques such as quantization, pruning, and compilation
  • Experience integrating LLM function calling or tool execution with structured outputs
  • Hands-on experience with Android system services or Android Automotive OS (AAOS)
  • Deep understanding of edge constraints including real-time behavior and memory pressure
Apply on Applied Intuition websiteSave

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