We need a hands-on ML engineer to implement cutting-edge research in uncertainty quantification and build our open-source confidence calibration toolkit. You'll turn papers into production code that thousands of developers will use. Required Skills Strong PyTorch experience (2+ years hands-on) Paper implementation experience - you've implemented at least 3 research papers Python expertise - clean, efficient, documented code GPU optimization - experience with CUDA, mixed precision training Git proficiency - comfortable with collaborative development Primary Responsibilities Implement research papers in PyTorch (focus on confidence calibration methods) Develop and maintain our confidence-kit open-source library Run large-scale experiments on model calibration Optimize inference performance for production use Debug and fix issues in neural network implementations What We Offer Paper co-authorship - build your research profile GitHub fame - your code used by thousands globally Flexible hours - no micromanagement Learning budget - ₹1,500/month or R$100/month Milestone bonuses - up to 20% monthly bonus Conference attendance - virtual passes to NeurIPS, ICML, ICLR Equipment (additional) support after 3 months
We need a hands-on ML engineer to implement cutting-edge research in uncertainty quantification and build our open-source confidence calibration toolkit. You'll turn papers into production code that thousands of developers will use. Required Skills Strong PyTorch experience (2+ years hands-on) Paper implementation experience - you've implemented at least 3 research papers Python expertise - clean, efficient, documented code GPU optimization - experience with CUDA, mixed precision training Git proficiency - comfortable with collaborative development Primary Responsibilities Implement research papers in PyTorch (focus on confidence calibration methods) Develop and maintain our confidence-kit open-source library Run large-scale experiments on model calibration Optimize inference performance for production use Debug and fix issues in neural network implementations What We Offer Paper co-authorship - build your research profile GitHub fame - your code used by thousands globally Flexible hours - no micromanagement Learning budget - ₹1,500/month or R$100/month Milestone bonuses - up to 20% monthly bonus Conference attendance - virtual passes to NeurIPS, ICML, ICLR Equipment (additional) support after 3 months
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.,