Reolink - Data Scientist / Large Model Algorithm Engineer
Requirements
• PhD Holder in Computer Science, Applied Mathematics, Electrical Engineering, Pattern Recognition, Artificial Intelligence, Automatic Control, Operations Research, Biology, Physics / Quantum Computing, Neuroscience, Statistics or a related field. • Holder • Familiar with common machine learning and deep learning algorithms and keeping track with the latest SOTA implementations. • SOTA implementations • Strong programming skill in Python, C / C++, proficient in mathematical / statistical concepts and exceptional coding skills • Python, C / C++ • Hands-on experience with AI / ML frameworks be familiar such as Caffe, PyTorch, TensorFlow, MxNet etc. • AI / ML frameworks • Caffe, PyTorch, TensorFlow, MxNet etc. • Have rich project experience in machine learning and deep learning, be familiar with common algorithm models, such as CNN, RNN, LSTM, Transformer, ViT, etc., and be able to improve and innovate models according to actual problems. • machine learning • deep learning • CNN, RNN, LSTM, Transformer, ViT, etc., • Experience in familiar the design, parameter tuning and optimization methods of neural network models is a plus • Experience in model compression and in the transplantation and optimization of deep learning forward inference on various platforms, including NPU / GPU / DSP / ARM on mobile platforms and CPU / GPU on server platforms is also a plus. • NPU / GPU / DSP / ARM on mobile platforms • CPU / GPU on server platforms • Strong logical thinking and problem-solving ability, able to independently undertake the research and tackling of complex problems and team spirit are highly valued traits.
Responsibilities
• Algorithm Development: Development and optimization of machine learning and deep learning algorithms to solve complex business problems. • Algorithm Development • Model Training and Evaluation: Participate in the construction and training of artificial intelligence models, including but not limited to neural networks, decision trees, etc., evaluate and optimize the models to improve their performance and accuracy. • Model Training and Evaluation • Data Analysis and Preprocessing: Conduct data collection, cleaning, preprocessing and feature engineering, provide high-quality data for model training, and arrange and guide data labelers to carry out data labeling and other work. • Data Analysis and Preprocessing • Collaboration and Deployment: Collaborate with team members to integrate algorithm models into actual business systems, promote the implementation of projects, and realize the intelligent upgrading of products. • Collaboration and Deployment • Research and Innovations: Stay up to date with the latest advancements in AI and ML technologies. Explore and deploy new technologies, drive innovations to create value to market needs, and enhance the company’s competitiveness. Audio and video algorithms for surveillance/monitoring applications which also include the research and implementation of algorithms involving target detection, feature extraction, tracking, and recognition. • Research and Innovations • Documentation and Reporting: Maintain comprehensive documentation of algorithms, experiments, and project workflows. Communicate results and findings to technical and non-technical stakeholders. • Documentation and Reporting
Benefits
• Entitled to Yearly Bonus & Performance Bonus
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