Ai CTO

**1. Strategic Leadership & Technological Vision**:

- **Develop and drive**the long-term technical strategy, research direction, and roadmap of the AI Lab, aligning closely with the company’s overall business objectives.
- **Stay ahead of trends**in pathological AI, such as multimodal learning, self-supervised learning, explainable AI, and digital pathology analysis. Assess the potential of emerging technologies and guide the team in forward-looking research initiatives.
- **Represent the company**in academic and industry collaborations with leading institutions and enterprises to enhance the company’s technological influence in the pathology AI domain.

**2. R&D Management & Innovation**:

- **Lead core AI research and development**: Oversee critical pathology AI projects, including cytology AI, companion diagnostics AI platforms, histopathology foundation models, predictive modeling (e.g., for pharma partners), and multimodal AI models. Responsible for the research, design, implementation, and optimization of core AI algorithms.
- **Foster innovation**: Create an environment that encourages innovation. Lead the team in tackling major technical challenges such as few-shot learning, model generalizability, explainability, and handling complex biological variations. Deliver high-quality patents and academic publications.
- **Data management**: Manage the company’s pathology data end-to-end, transforming data into valuable digital assets, and maximizing data efficiency and utility.
- **Productization focus**: Ensure R&D efforts are closely aligned with product requirements and clinical value. Drive the efficient translation of research outcomes into deployable, regulated product solutions (e.g., compliant with medical software device regulations).
- **Technology evaluation & adoption**: Evaluate and integrate cutting-edge AI frameworks, tools, and infrastructure to improve R&D efficiency and model performance.

**3. Team Building & Management**:

- **Recruit, mentor, and manage**a high-performing AI R&D team (including researchers, algorithm engineers, data scientists, etc.).
- **Establish effective development processes**, coding standards, and quality benchmarks (e.g., code reviews, model versioning, experiment tracking).
- **Provide technical leadership and career guidance**, nurturing a team culture of collaboration, continuous learning, and pursuit of excellence.

**4. Cross-functional Collaboration**:

- **Work closely with the product team**to understand product requirements and definitions, ensuring AI solutions effectively support product goals.
- **Collaborate deeply with medical teams**, including pathologists, clinicians, device and reagent teams, to thoroughly understand clinical needs, workflows, diagnostic criteria, and data characteristics. Ensure the clinical relevance and practicality of AI models, and contribute to the implementation of the company’s “Four Modernizations” strategy.

**Qualifications**:
**Educational Background**:
**Work Experience**:

- **6+ years of experience**in AI, machine learning, deep learning, or computer vision, with **at least 3 years of experience leading AI R&D teams**(team size: 5+).
- **Required**: Demonstrated experience in **successfully transitioning AI models from research to product deployment**, ideally including regulatory approval.

**Technical Expertise**:

- **Advanced AI/ML skills**: Proficient in deep learning (e.g., CNNs, Transformers, GANs), ML theory, and algorithm design. Publications at top-tier conferences (e.g., CVPR, MICCAI, NeurIPS, ICML, ICLR, ECCV) are a plus.
- **Strong programming skills**: Expert in Python and well-versed in major deep learning frameworks such as PyTorch or TensorFlow. Familiar with Linux environments and software engineering best practices.
- **Computer vision specialization**: Solid experience in image processing, segmentation, detection, classification, and feature extraction.
- **Data processing proficiency**: Skilled in handling large-scale and complex datasets, particularly image data.
- **Model development lifecycle**: Deep understanding of the end-to-end lifecycle of model training, tuning, validation, deployment, and monitoring.
- **Regulatory knowledge**: Familiarity with medical AI regulations and quality systems (e.g., ISO 13485, FDA SaMD guidance, CE IVDR/MDR, AI Act) is a strong advantage.

**Core Competencies**:

- **Exceptional leadership & strategic thinking**: Ability to inspire teams, define a clear vision, and drive effective execution.
- **Outstanding communication & coordination skills**: Able to clearly articulate complex technical concepts to diverse audiences including technical, medical, product, and executive stakeholders; skilled in cross-functional collaboration.
- **Strong problem-solving ability**: Capable of analyzing complex challenges, crafting innovative solutions, and executing effectively.
- **Results-driven with strong execution**: Able to deliver high-quality results in a fast-p