My client is building software to deliver multi-stakeholder AI using the power of blockchain technology. As a lead machine learning scientist, you would have the opportunity to work at the intersection of these fascinating fields and shape the future of machine learning by allowing the sharing of information without compromising peoples' privacy, security or sovereignty. This challenge involves a combination of game theory, differential privacy, machine learning, cryptography and distributed computing. They are looking for someone to lead the team of highly talented machine learning scientists and engineers to help bring their vision to life.
The position entails a blend of practical machine learning problem-solving, foundational algorithm development, and protocol design. Their research scientists are afforded the chance to publish in renowned conferences and collaborate closely with our seasoned engineers to deliver tomorrow's state-of-the-art technologies. They are particularly interested in conversing with machine learning experts who possess extensive technical expertise in the field and are either currently leading a team or prepared to take on leadership responsibilities. Candidates with experience in any of the following areas-MLOps, game theory, federated learning, differential privacy, or cryptography-are highly encouraged to submit their applications.
Essential Skills and Experience:
Minimum of 5 years of hands-on experience in deep learning, game theory, multi-agent systems, or another relevant branch of AI.
Proven track record of designing innovative algorithms to tackle real-world problems in either research or industry settings.
Ability to collaborate effectively with diverse stakeholders, including customers, open source developers, and investors, to develop new products and features based on our collective learning technology.
Awareness of current industry trends and a drive to develop technologies that address emerging product niches.
Extensive expertise in modern machine learning frameworks such as PyTorch and TensorFlow.
Strong comprehension of the fundamentals of machine learning, encompassing optimization, probability theory, and statistics.
Dedication to software engineering, with proficient coding skills in Python.
Experience in the complete machine learning life cycle, spanning data visualization, algorithm design, development, deployment, and monitoring.
This is a Hybrid role Based in Cambridge and they are not able to offer sponsorship.