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

- **Company:** Applied Intuition
- **Location:** Sunnyvale, CA, United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Android Software Development, C++ (Programming Language), Java Native Interface, Tensorflow, Large Language Models, Discretization, Information Technology, ONNX (Open Neural Network Exchange) Format, Embedded AI, Machine Learning Operations, Invoking Functions
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_bef77bc732e139978c62a419353a4e41

## About the Role

* 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, * 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

## Description

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., * 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