We are looking for someone who is passionate and enthusiastic about Machine Learning verticals. Someone who is proficient in theoretical analysis of machine learning algorithms - especially deep reinforcement learning algorithms and experience in multi-agent reinforcement learning.
We are also looking for someone who has a knowledge base of Bayesian statistics. Along with hands-on experience with at least one of the following: Python, C/C++, Java, etc. (PyTorch and Tensorflow). And professional experience in microservices-based deployment of AI algorithms on cloud platforms (GCP, AWS, or Azure).
You will lead advanced exploratory research and development projects in reinforcement learning and machine learning and related fields to create highly innovative customer solutions, so having a strong mathematical and machine learning background is key.
- Work with external collaborators (universities and startups) and foster relationships in reinforcement learning domains, multi-agents systems, control systems, game theory, and other topics of relevance to machine learning and theoretical statistics.
- Conduct theoretical/empirical research and development into sequential decision making and large-scale, open-ended learning.
- Prototype and productize microservices
- Contribute to the research community by publishing papers in machine learning
- Provide research that can be applied to internal product development
- Communicating with executives and customers explaining the technology and its business/customer implications
- Individual Contributor position that requires mentorship of Ph.D. level scientists and engineers.
- PhD degree in Computer Science, Engineering, Mathematics, or Statistics.
- Postdoctoral training in machine learning.
- A proven record of publications (at least a few first-author papers in reputed conferences and journals - such as NeurIPS, COLT, UAI, ICML, AAMAS, AISTATS, JMLR, PAMI, and others)
- Machine Learning
- Algorithm Development
- Reinforcement Learning
- theoretical analysis
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