On-Device AI Engineer for Android Automotive

InvestedintheMission; Sunnyvale (CA); Hybrid; USD 190,000 - 260,000; Full time

Benefits offered by this job

Equity

Health insurance

401k match

Paid time off

Job summary

Applied Intuition, Inc. is 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 under latency, memory, and safety constraints. You will deploy production ML on AAOS, implement multimodal LLMs, and optimize for real-time edge environments, interfacing with vehicle signals via C++ and JNI.

Qualifications

• 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.

Responsibilities

• Deploy 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.

Skills

C++

JNI

Embedded ML

Android AAOS

Model optimization

LLM integration

Real-time systems

Edge computing

Education

BS/MS/PhD in CS/EE or related field

Tools

TensorFlow Lite

ONNX Runtime

Job description

Applied Intuition, Inc. is 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 under latency, memory, and safety constraints. You will deploy production ML on AAOS, implement multimodal LLMs, and optimize for real-time edge environments, interfacing with vehicle signals via C++ and JNI.