Embedded AI Engineer – Android Automotive (On-Device Intelligence)
Applied Intuition4 months ago
Sunnyvale, CA, USAMid Level
H1B Sponsor
Base Salary
$183k - $253k/yr
Responsibilities
- Deploy and run production-grade ML inference and learning systems on Android Automotive.
- 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 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 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.
- At least 3 years of experience shipping ML inference on embedded, mobile, or automotive platforms.
- Strong proficiency in C++ and experience with native Android integration using JNI.
- Expertise in quantization, pruning, compilation, and other model optimization techniques.
- Experience integrating LLM function calling or tool execution with structured outputs.
- Hands-on experience with Android system services or Android Automotive OS.
- Deep understanding of edge constraints, including real-time behavior and memory pressure.
- Preferred experience with Snapdragon Automotive, ARM Ethos, or specialized NPU pipelines.
- Preferred experience running quantized LLMs on-device using llama.cpp or TFLite transformers.
- Familiarity with functional safety concepts such as ISO 26262, sandboxing, or policy enforcement is preferred.
- Preferred experience bridging cloud-trained models to resource-constrained embedded runtimes.
Benefits
- Primarily in-office work 5 days per week, with occasional remote-work flexibility.
- Health, dental, vision, life, and disability insurance.
- 401(k) retirement benefits with employer match.
- Learning and wellness stipends.
- Paid time off.
- Full-time position.