AI軟體研發工程師-竹北

1. AI-Driven Log Triage: Design and implement NLP (Natural Language Processing) or pattern-recognition models to automatically classify, filter, and extract root-cause signatures from massive daily SSD failure logs.
2. Anomaly Detection & Fuzzing: Develop ML-based fuzzing frameworks to autonomously generate boundary-pushing NVMe/PCIe commands to proactively uncover zero-day firmware vulnerabilities.
3. Digital Twin Emulation: Analyze complex telemetry and trace data from top-tier datacenter customers to build predictive models and digital twin testing workloads.
4. Data Pipeline Optimization: Build robust data pipelines to aggregate test results from 100+ concurrent testing servers into a centralized, queryable database for SVD dashboards.
5. Cross-Functional Collaboration: Partner directly with SSD domain experts (Firmware Validation Leads) to translate hardware testing pain points into software/AI solutions.

學歷：大學以上
1. Master’s or Bachelor’s degree in Computer Science, Data Science, Statistics, Electrical Engineering, or a related field.
2. 3+ years of hands-on experience in Machine Learning, Data Mining, or AIOps.
3. Strong Programming Skills: Proficiency in Python is mandatory. Familiarity with C/C++ or Go is a plus.
4. AI/ML Frameworks: Solid experience with ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch, Pandas, NumPy).
5. Data Engineering: Experience dealing with unstructured data, text parsing (Regex, NLP), and time-series data analysis.
6. Experience developing LLM-based applications.

加分條件
1. Basic knowledge of computer architecture, operating systems (Linux kernel), or storage interfaces (PCIe, NVMe, SATA, USB).
2. Experience in QA automation, CI/CD pipelines (Jenkins, Git), or software testing methodologies.
3. Experience with LLMs (Large Language Models) or RAG (Retrieval-Augmented Generation) for enterprise internal tool development.