Artificial Intelligence Engineer

Key Responsibilities

Contribute to building and optimizing speech model (ASR, TTS etc.) and application pipelines (Prompt, RAG, tool calling, service orchestration) to improve scenario performance and response stability.

Participate in end to end development and integration of AI features for wearables, covering requirement decomposition, solution design, joint debugging/testing, and version iteration.

Engage in on-device model deployment and performance optimization, including model conversion, quantization compression, inference acceleration, and resource tuning (latency, memory, power consumption).

Support the engineering development of AI Agents, implementing capabilities such as task planning, tool invocation, and multi-turn memory in device scenarios.

Collaborate with product, algorithm, system, and testing teams on data analysis, effect evaluation, issue diagnosis, and closed-loop improvement.

Contribute to engineering best practices, including evaluation baselines, logging/monitoring, graceful fallback, release management, and stability assurance mechanisms.

Requirements

Bachelor's degree or above in Computer Science, Artificial Intelligence, Software Engineering, Electronic Information, Automation, or related fields.

Solid understanding of Deep Learning architectures (Transformer, Conformer, RNN-T, BERT) and familiarity with mainstream ASR/speech technologies or LLM frameworks.

Solid programming skills with proficiency in at least one of Python, C++, or Java.

Familiar with at least one AI application development approach (e.g., large-model API integration, RAG, Prompt engineering, workflow orchestration).

Interest in on-device AI engineering; familiarity with any of ONNX, TFLite, NCNN, MNN, or ONNXRuntime is a plus.

Basic engineering debugging capabilities, able to independently diagnose common issues (performance fluctuations, API timeouts, resource anomalies, stability problems).