On-Device AI Engineer for Android Automotive

Applied Intuition; Sunnyvale (CA); Hybrid; USD 150,000 - 250,000; Full time

Benefits offered by this job

Equity in options/RSUs

Comprehensive health insurance

401k with match

Paid time off

Job summary

Applied Intuition in Sunnyvale is seeking a hands-on Embedded ML Engineer to own end-to-end on-device ML systems for a next-generation Android Automotive platform. You will ensure models run reliably within real-world constraints such as latency, memory, power, and safety requirements.

You will deploy production-grade ML inference, develop multimodal LLMs, and integrate models with TensorFlow Lite, ONNX Runtime, and vendor SDKs, while optimizing performance across CPU, GPU, and NPU and

Qualifications

• BS/MS/PhD in CS, EE, or related field.
• 3+ years shipping ML inference on embedded/mobile/auto platforms.
• Strong proficiency in C++ and native Android integration (JNI).
• Expertise in model optimization (quantization/pruning/compilation).
• Experience integrating LLM function calling with structured outputs.
• Hands-on experience with Android system services or AAOS.
• Deep understanding of edge constraints like real-time behavior and memory pressure.

Responsibilities

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

Skills

C++

JNI

AAOS

ML inference

Model optimization

LLM function calling

Android integration

Education

BS/MS/PhD in CS/EE

Tools

TensorFlow Lite

ONNX Runtime

llama.cpp

Job description

Applied Intuition in Sunnyvale is seeking a hands-on Embedded ML Engineer to own end-to-end on-device ML systems for a next-generation Android Automotive platform. You will ensure models run reliably within real-world constraints such as latency, memory, power, and safety requirements.

You will deploy production-grade ML inference, develop multimodal LLMs, and integrate models with TensorFlow Lite, ONNX Runtime, and vendor SDKs, while optimizing performance across CPU, GPU, and NPU and