1.Data Collection and Preparation:
- Collect, curate, and annotate large datasets for training AI models.
- Ensure data quality and integrity through preprocessing and cleaning.
2. Model Development and Training:
- Design and implement machine learning models using various algorithms.
- Knowledge to optimized model to fit with low configuration of AI inference machine.
- Knowledge about data augmentation and it benefits to training.
- Train models on prepared datasets and fine-tune parameters to enhance performance.
- Utilize frameworks such as PyTorch, TensorFlow, or Keras for model development.
3. Performance Evaluation and Optimization:
- Evaluate model performance using metrics like accuracy, precision, recall, and F1 score and knowledge about most of useful SOTA metrics.
- Optimize models for efficiency and performance.
- Conduct experiments to compare different models and approaches.
- Experience in deploying ML/DL applications on specific embedded platforms (e.g., STM32 AI, NVIDIA Jetson, NXP, etc.) is a strong plus.
- Debugging skills for embedded targets is a plus.
- Experience with RTOS or Linux programming is a plus.
4. Collaboration and Communication:
- Work closely with data scientists, software engineers, and other stakeholders.
- Communicate findings and progress to team members and management.
- Collaborate on integrating AI models into existing systems and applications.
- Knowing system integration is a plus.
5. Continuous Learning and Improvement:
- Stay updated with the latest advancements in AI and machine learning.
- Capability to read and understand SOTA papers.
- Participate in workshops, conferences, and training sessions.
- Implement new techniques and tools to improve model performance and development processes.
