ML Research Engineer

As a hands-on ML engineer at our company, you will be responsible for implementing cutting-edge research in uncertainty quantification and contributing to the development of our open-source confidence calibration toolkit. Your main focus will be on turning research papers into production code that will be utilized by thousands of developers. **Key Responsibilities:** - Implement research papers in PyTorch, with a specific focus on confidence calibration methods - Develop and maintain our confidence-kit open-source library - Conduct large-scale experiments on model calibration - Optimize inference performance for production use - Debug and resolve issues in neural network implementations **Qualifications Required:** - Strong PyTorch experience (2+ years hands-on) - Previous experience in implementing research papers, having worked on at least 3 papers - Proficiency in Python for writing clean, efficient, and well-documented code - Experience in GPU optimization, including familiarity with CUDA and mixed precision training - Proficient in using Git for collaborative development At our company, we offer a range of benefits to our employees, including: - Opportunities for paper co-authorship to enhance your research profile - Recognition on GitHub with your code being used by a global audience - Flexible working hours with no micromanagement - Learning budget of 1,500/month or R$100/month - Milestone bonuses of up to 20% monthly - Virtual passes to conferences like NeurIPS, ICML, ICLR - Additional equipment support after 3 months of employment Join us in this exciting opportunity to work on cutting-edge ML research and contribute to the development of impactful tools for the developer community.,