Intelligent Cabin AI Full-Stack Engineering

Brief description of the position:

This role involves developing and optimizing LLM applications, building AI Agent systems with multimodal and tool-use capabilities, and deploying AI on edge/embedded devices. It requires strong software engineering skills, hands-on LLM fine-tuning experience, and the ability to integrate AI across cloud and hardware platforms.

Key Tasks:

Define and implement the end-to-cloud collaborative architecture for LLMs/VLMs, and fine-tune open-source models for automotive scenarios using SFT, LoRA, and RLHF. (Main Responsibility)

Construct high-quality in-vehicle training datasets and automated data pipelines to support continuous model iteration and performance evaluation. (Main Responsibility)

Design an intelligent cabin agent orchestration layer to enable complex intent decomposition and autonomous cross-application tool use. (Main Responsibility)

Implement multimodal fusion interaction (voice, vision, gesture, biometrics) to deliver immersive experiences such as gaze-based wake-up, emotion recognition, and proactive contextual responses. (Main Responsibility)

Build a user profiling and long-term memory system leveraging vector databases, knowledge graphs, and RAG architectures to enhance knowledge grounding and personalized experiences. (Main Responsibility)

Optimize model inference on automotive SoC platforms through quantization, pruning, and kernel-level tuning to ensure low latency and high energy efficiency. (Main Responsibility)

Balance cloud costs and edge responsiveness by optimizing API efficiency and system resource utilization. (Main Responsibility)

Design and implement robust AI guardrail systems to mitigate hallucinations and ensure determinism of vehicle control commands. (Main Responsibility)

Enforce strict user data privacy protection and comply with automotive-grade AI safety standards. (Main Responsibility)

Develop scalable AI backend services using Python and Docker containerization, and integrate sensors, actuators, and in-vehicle communication protocols for hardware-software co-development. (Main Responsibility)

Qualification and skills required for the position:

Education: Bachelor's degree or above in Computer Science, Artificial Intelligence, Robotics, or a closely related field (embedded systems, edge computing, full-stack AI system etc.).

Knowledge, skills and others:

Proficient in C++/Python; Rust, Embedded C, or RTOS experience is a plus.

Skilled in edge AI inference (TensorRT/ONNX/TVM) and model optimization (quantization/distillation).

Hands-on with RAG, fine-tuning, and Agent development (tool use, memory).

Familiar with SOA, SOME/IP, DDS, and embedded Linux.

Proven experience deploying large models on resource-constrained edge devices.

Strong HMI sense and passion for AI-driven user experiences.

Smart cockpit experience at OEMs/Tier-1 preferred.

Independent worker with strong communication and AI-hardware integration skills.