Amber Group is a global leading digital asset company providing crypto financial services to both institutional and high-net-worth investors globally. 

We offer best-in-class liquidity solutions and cutting-edge trading infrastructure across major exchanges, applications, and networks. With over $1 trillion in cumulative trading volume, our deep liquidity helps power the digital asset ecosystem. 

Beyond trading, our full-suite of offerings includes wealth management, lending and investing products. But at our core, we focus on building strong relationships and delivering personalized service to help clients navigate this fast-growing industry.

At Amber, security is our #1 priority. We have invested years of effort and millions of dollars in cybersecurity, crypto-security, and operational security across the firm, with industry-leading certifications like SOC 2 Type II and ISO 27001.

Powered by a 400+ team of traders, technologists and engineers operating 24/7 globally, our technology and research capabilities are world-class. Yet we remain entrepreneurial, always seeking fresh ideas and risks worth taking. We are always interested in people who have an appetite for taking calculated risk, demonstrate a high level of original thinking and intellectual curiosity.   

Role and Responsibilities:

• Conduct cutting-edge research in artificial intelligence (AI) modeling and machine learning (ML) algorithms, specifically tailored for crypto investment.
• Develop, implement, and validate quantitative models using ML techniques - particularly deep learning, to enhance crypto investment strategies.
• Collaborate with the technology team to deploy predictive models into production, ensuring seamless integration with the existing low-latency trading system.
• Train and optimize a variety of models, including linear models, tree-based models, and advanced deep learning architectures.
• Evaluate the strengths and limitations of different models to ensure their effective application in trading strategies.

Qualifications:

• Master's or PhD degree in Computer Science (preferred), Artificial Intelligence, Physics, Statistics, or other relevant technical fields.
  
• Strong knowledge of machine learning techniques, especially deep learning, and their practical applications.
  
• Demonstrated academic contributions, with publications in top-tier ML conferences or journals, and a reasonable number of citations on Google Scholar.
  
• Solid programming skills in C++ (preferred) and Python.
  
• Experience in low- or high-frequency trading is a plus.