Embedded AI Engineer

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

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

Preferred Qualifications

• 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

About the Company

Applied Intuition is powering the future of physical AI, creating the digital infrastructure needed to bring intelligence to every moving machine on the planet across automotive, defense, trucking, construction, mining, and agriculture industries.