Hongkong
Shenzhen
Shanghai
Singapore
Hong Kong
1. Responsible for the development and maintenance of deep neural learning algorithms for medical instruments, including data cleaning, model optimization, embedded GPU transplantation optimization, performance evaluation, etc.;
2. Responsible for the system architecture design of the deep learning processing pipeline, cooperate with the algorithm team to integrate and optimize the target system;
3. Responsible for writing relevant technical documents, and coordinating test engineers to develop and test test cases.
1、Master degree or above in computer science, electronic information, software or related professional and technical background,
2、At least 2 years of experience in software development.
3、Master the basic methods of computer vision & machine learning, with strong algorithm implementation ability.
4、Proficient in C / C + +, data structure and design mode, with good software engineering habits.
5、Proficient in GPU programming, such as OpenCL, CUDA, etc.; master the optimization methods for common technical problems such as parallel algorithm processing and memory usage.
6、Strong learning ability, strong hands-on ability, familiar with the use of oscilloscope, logic analyzer and other related test instruments.
7、Strong sense of responsibility, strong sense of teamwork, have to deal with complex problems independently.
8、Priority should be given to those with strong research ability, strong code ability, rich relevant experience, or from well-known laboratories in the fields of computer vision / machine learning / computer graphics and data mining.
Shanghai · Zhangjiang
1. System Design & Documentation: Responsible for the design of clinical AI algorithm systems and the writing of technical documentation; participate in the evaluation of medical AI solutions and technical route selection.
2. Data & Model Development: Responsible for the cleaning, annotation standard formulation, and feature engineering of medical data (Sequencing/Protein/Pathology/EHR, etc.); lead the training, validation, and performance evaluation of core algorithm models.
3. Architecture & Optimization: Design the architecture for clinical decision support systems based on Deep Learning/LLMs, including module development and simulation. Collaborate with clinical experts and software teams to optimize algorithms for real-world clinical scenarios; assist in the deployment and acceleration of algorithms on servers or edge devices.
4. Testing & Validation: Conduct system-level algorithm testing and debugging; participate in multi-center clinical validations and provide technical support for documentation related to medical device software registration (e.g., NMPA/FDA/CE).
5. Quality & Control: Participate in algorithm performance optimization, inference cost control, risk management, and quality assurance.
1. Education: Master’s degree or higher in Computer Science, Biomedical Engineering, AI, or Medical Informatics. (Exceptional candidates with a Bachelor's degree and 2+ years of practical experience will be considered). Solid theoretical foundation in Machine Learning and Deep Learning is required.
2. Technical Skills: Proficient in Python for algorithm development; familiar with mainstream Deep Learning frameworks such as PyTorch or TensorFlow. Ability to independently design model architectures or coordinate with data engineering teams for data governance.
3. Domain Expertise: Familiarity with medical data analysis (segmentation, detection, classification) or Clinical NLP (EHR structuring, knowledge graphs). Mastery of mainstream architectures like Transformer, CNN, and Diffusion models. Knowledge of Multi-modal LLMs (MLLM) or AI Agent stacks is a strong plus.
4. Standards & Tools: Understanding of medical data processing standards (DICOM, HL7, FHIR), model evaluation methods, external validation, and generalization analysis. Familiarity with medical AI compliance and ethics. Proficiency in Linux, Docker, and Git is required.
5. Soft Skills: Strong communication skills and cross-disciplinary collaboration awareness; able to effectively communicate with clinical personnel from non-technical backgrounds. Good English proficiency (reading/writing) to review research papers and reproduce key technologies is preferred.
上海 · 张江
1.负责硬件系统设计及相关文档撰写；参与硬件解决方案评估及器件选型；
2.负责电路原理图，PCB设计，硬件调试及测试；
3.负责基于FPGA的控制系统架构设计，模块编写，系统仿真验证；配合软硬件工程师，优化和调试FPGA硬件设计以满足软硬件接口需求；配合图像算法在FPGA上的硬件加速实现和验证；
4.产品系统级测试与调试及参与EMC、安规测试；
5.参与硬件成本控制.风险控制和质量控制。
1.自动化、电子类、半导体技术相关专业，2年及以上工作经验，具备扎实的数模电、电路理论基础；
2.熟练使用EDA软件进行电路原理图设计，可以自主设计Layout或者有对接外发layout工作的经验；
3.熟悉STM32 、PIC等系列MCU，精通硬件电路设计和底层软件设计；
4.掌握XILINX / ALTERA FPGA硬件结构、设计流程及开发工具；熟悉FPGA接口：SPI、IIC、GTX、LVDS、DDR3、BT1120，AXI4；熟悉常用图像接口：MIPI CSI ;MIPI DSI ;DVP；熟练使用Verilog或VHDL语言的优先；
Singapore
1. Strategic Business Development: Formulate and execute comprehensive market entry strategies for key regions (e.g., Southeast Asia, Europe, Middle East). Identify and capitalize on new business opportunities to achieve revenue targets and market share growth.
2. Regulatory & Certification Management: Lead the strategic planning and execution of international regulatory approvals. Oversee the certification process for key markets, ensuring compliance with standards such as CE (MDR), FDA, HSA, and other regional medical device regulations.
3. Commercialization Strategy: Drive the commercial launch of new products, including pricing strategy, value proposition definition, and go-to-market planning tailored to specific healthcare ecosystems.
4. Channel & Partnership Development: Identify, negotiate with, and onboard high-quality distributors, agents, and OEM partners. Build and maintain a sustainable global channel network to ensure effective market penetration.
5. Stakeholder Management: Establish and maintain strong relationships with key opinion leaders (KOLs), hospital procurement departments, government bodies, and industry associations to enhance brand visibility and credibility.
6. Market Intelligence: Monitor industry trends, competitor activities, and healthcare policies to provide strategic insights for product roadmap and business planning.
1. Education: Bachelor's degree in Biomedical Engineering, Life Sciences, or Business Administration. An MBA or advanced degree is highly preferred.
2. Experience: Minimum of 8-10 years of experience in the medical device industry, with at least 5 years in a senior business development or commercial leadership role. Proven track record of successfully launching medical devices in international markets.
3. Regulatory Knowledge: Deep understanding of global medical device regulatory frameworks (CE Mark, FDA 510(k), ISO 13485) and the ability to navigate the certification lifecycle efficiently.
4. Network: Established network of contacts within the healthcare industry, including distributors, hospital networks, and regulatory bodies, particularly in Asia-Pacific or European markets.
5. Language & Communication: Fluent in English (native or bilingual proficiency). Excellent negotiation, presentation, and interpersonal skills with the ability to influence stakeholders at all levels.
6. Travel: Willingness to travel internationally (approx. 30-40%) to visit markets, attend conferences, and meet with partners.
