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

Applied Intuition•Sunnyvale, CA

2d•Onsite

About The Position

We are building on-device intelligence for a next-generation Android Automotive platform. This role owns the end-to-end lifecycle of embedded ML systems, ensuring models behave predictably and safely in production environments under real-world constraints such as latency, thermal limits, and functional safety. At Applied Intuition, you will:

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 Haves

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

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