Applied Intuition
Embedded AI Engineer – Android Automotive (On-Device Intelligence)
About this role
Applied Intuition seeks an Embedded AI Engineer to develop on-device intelligence for Android Automotive, managing the full lifecycle of production ML systems optimized for real-world constraints like latency, thermal limits, and safety. You'll deploy multimodal LLMs, integrate ML inference frameworks, and design safety guardrails for vehicle-integrated AI.
What you'll do
- Deploy production ML inference and learning systems on Android Automotive (AAOS)
- Implement on-device multimodal LLMs with schema design and safe vehicle API dispatch
- Integrate models using TensorFlow Lite, ONNX Runtime, or vendor SDKs
- Profile and optimize models for latency, memory, power, and thermal budgets
- Design safety boundaries and guardrails for model outputs with fallback logic
- Interface with vehicle signals, sensors, and system services using C++ and JNI
What they're looking for
- C++ and native Android integration (JNI)
- ML inference on embedded and mobile platforms
- Model optimization (quantization, pruning, compilation)
- LLM function calling and tool execution
- Android Automotive OS (AAOS) system services
- Edge computing constraints and real-time behavior
- Performance instrumentation across CPU, GPU, and NPU
- Multimodal LLM deployment
Benefits
- Competitive base salary ($150,000–$250,000 USD annually)
- Equity compensation (stock options/RSUs)
- Comprehensive health, dental, vision, life, and disability insurance
- 401(k) retirement plan with employer match
- Learning and wellness stipends
- Paid time off
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Applied Intuition
Applied Intuition builds autonomous vehicle and defense systems software, including motion planning algorithms, simulation infrastructure, and autonomy integration platforms for aerial and ground platforms. The company is hiring for security engineers, robotics/autonomy software engineers, hardware-in-the-loop specialists, and IT operations professionals to support its growing physical AI operations.
- Website
- appliedintuition.com
Likely interview questions
- Walk us through your experience shipping ML inference on embedded or mobile platforms. What were the key optimization challenges you faced, and how did you address latency and memory constraints?
- Describe your experience with model optimization techniques like quantization and pruning. How have you measured the trade-offs between model accuracy and inference performance on resource-constrained devices?