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Applied IntuitionApplied IntuitionSunnyvale, CA

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

Develops and deploys embedded ML systems and on-device multimodal LLMs for Android Automotive platforms, optimizing for edge constraints like latency and safety. Requires 3+ years shipping ML on embedded/mobile platforms, C++ proficiency, and Android expertise.

150k – 250k/yr
On-site3+ YOEEmbedded Engineering

About the role

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

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

Nice to Have

  • Experience with Snapdragon Automotive, ARM Ethos, or specialized NPU pipelines
  • Background in running quantized LLMs on-device using llama.cpp or TFLite transformers
  • Familiarity with functional safety concepts (ISO 26262), sandboxing, or policy enforcement
  • Experience bridging cloud-trained models to resource-constrained embedded runtimes

Compensation

Base salary range: $150,000 - $250,000 USD annually, plus equity and benefits.

Skills

C++JniTensorflow LiteOnnx RuntimeAndroid Automotive OsMl InferenceModel OptimizationQuantizationPruningLLMsGPUNpuIso 26262Llama.Cpp

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