Applied Intuition
Embedded AI Engineer – Android Automotive (On-Device Intelligence)
$183K–$253K
Sunnyvale · On-site · Full-time · Posted Apr 8, 2026
- Experience
- 3+ years shipping ML inference on embedded, mobile, or automotive platforms.
- Education
- BS, MS, or PhD in Computer Science, Electrical Engineering, or a related technical field.
About the job
Builds and deploys on-device machine-learning systems for Android Automotive, optimizing them for real-world constraints and safe production behavior.
What you'll do
- Deploy production ML inference and learning systems on Android Automotive and integrate models with runtime frameworks or vendor SDKs.
- Optimize and profile models for latency, memory, power, and thermal limits across CPU, GPU, and NPU.
- Implement multimodal LLMs with safe local vehicle API dispatch, guardrails, and integration with vehicle signals and services.
What they ask for
- 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 C++ proficiency and experience with native Android integration (JNI).
- Hands-on experience with Android system services or Android Automotive OS (AAOS).
Nice to have
- Experience with Snapdragon Automotive, ARM Ethos, or specialized NPU pipelines.
- Experience running quantized on-device LLMs using llama.cpp or TFLite transformers.
Pay
$183K – $253K USD base salary
Tools
Android Automotive (AAOS), TensorFlow Lite, ONNX Runtime, C++, JNI, Snapdragon Automotive, ARM Ethos, llama.cpp
AI focus
On-device embedded ML and multimodal LLM deployment, including structured tool execution, safe vehicle API dispatch, optimization, and runtime performance.
Model-written summary · the original posting is authoritative