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Bin **** AI Research Scientist

$600 / day


Bin Chen is a highly skilled professional with extensive experience and expertise across machine learning, deep reinforcement learning, and AI algorithm development. With a solid academic background including a PhD in Computer Science from the University of South Australia and prior education at Beijing University of Posts and Telecommunications and Queen Mary University of London, Bin has consistently demonstrated his proficiency in advancing AI technologies. His career highlights include roles such as a Reinforcement Learning Algorithm Researcher at InspirAI, where he contributed to cutting-edge research in integrating transfer learning and multi-agent systems. Bin’s industry experience as a Back-End Engineer at ZhongKe AI Cloud Technology Co., Ltd. and at major companies like and showcases his versatile skills in machine learning, Java back-end development, and system optimization.


Apache ThriftC++Cloud Computing Platform (Similar to AWS)Deep Reinforcement LearningETLHadoop (Map Reduce)JavaLinux OSLog4jMachine LearningMatplotlib)PHPPython (PyTorchRedisseabornSQLVersion Control (Git/SVN)
AI ConsultantAI Research ScientistDeep Learning EngineerMachine Learning EngineerReinforcement Learning Engineer


Jan 2021 - Aug 2024 Computer Science Doctor at University of South Australia
Aug 2012 - March 2015 Telecommunication and Information Systems Master at Beijing University of Posts and Telecommunications
Aug 2008 - Jan 2012 Telecommunications Engineering with Management Bachelor at Queen Mary University of London


March 2024 - June 2024 Practical Supervisor, COMP 2012, Data Structures Essentials at University of South Australia

– Supervised COMP 2012, Data Structures Essentials course at the University of South Australia.
– Guided students in understanding and implementing fundamental data structures like arrays, linked lists, stacks, queues, trees, and graphs.
– Provided hands-on support during lab sessions to enhance students’ proficiency in programming and algorithmic problem-solving.
– Offered constructive feedback and mentoring to help students develop effective coding practices.
– Collaborated with course instructors to ensure alignment between practical exercises and theoretical concepts.
– Facilitated a learning environment aimed at preparing students for real-world software development challenges.

March 2021 - Sept 2021 Reinforcement Learning Algorithm Researcher at InspirAI (

– Conducted research in reinforcement learning algorithms at InspirAI during internship tenure.
– Contributed to the development and enhancement of algorithms aimed at optimizing decision-making processes in dynamic environments.
– Investigated and implemented various reinforcement learning techniques such as Q-learning, policy gradient methods, and deep reinforcement learning.
– Collaborated with senior researchers to analyze experimental results and refine algorithmic approaches based on empirical findings.
– Documented research findings and contributed to technical reports and presentations aimed at sharing insights within the team.
– Engaged in discussions and brainstorming sessions to explore innovative solutions to complex problems in reinforcement learning.

March 2018 - Dec 2020 Back-End (PHP) Engineer at ZhongKe AI Cloud Technology Co

– Developed backend systems using PHP programming language at ZhongKe AI Cloud Technology Co., Ltd.
– Designed and implemented scalable and efficient server-side applications to support company operations and client needs.
– Integrated backend logic with front-end components and databases to ensure seamless functionality of web applications.
– Collaborated with cross-functional teams including front-end developers, designers, and project managers to deliver robust solutions.
– Maintained and optimized existing backend codebase, ensuring high performance and reliability of web services.

March 2015 - March 2018 Machine Learning Engineer at

– Developed and implemented machine learning algorithms and models to solve complex problems and improve business processes.
– Conducted data analysis, feature engineering, and model selection to achieve optimal predictive performance.
– Collaborated with data scientists, software engineers, and domain experts to integrate machine learning solutions into applications.
– Utilized Python libraries such as Scikit-learn, Pandas, and TensorFlow for data preprocessing, model training, and evaluation.
– Worked on tasks like classification, regression, clustering, and anomaly detection using supervised and unsupervised learning techniques.