Machine Learning Engineer (Remote)
As a remote machine learning engineer, you’ll work very closely with a senior member of our research team on cutting-edge deep learning research, infrastructure, and tooling towards the goal of creating general human-like machine intelligence.
• Implement a self-supervised network using contrastive and reconstruction losses.
• Create a library on top of PyTorch to enable efficient network architecture search.
• Open source internal tools.
• Implement networks from newly published papers.
• Work on tools for simple distributed parallel training of deep neural networks.
• Develop more realistic simulations for training our agents.
• Design automated methods and tools to prevent common issues with neural network training (e.g. overfitting, vanishing gradients, dead ReLUs, etc).
• Create visualizations to help us deeply understand what our networks learn and why.
• Very comfortable writing Python.
• Familiar with PyTorch and training deep neural networks.
• Excited to work on open source code.
• Passionate about engineering best practices.
• Self-directed and independent.
• Excellent at getting things done.
• Work directly on creating software with human-like intelligence
• Very generous compensation
• Flexible working hours
• Work remotely
• Time and budget for learning and self improvement
We started Generally Intelligent because we believe that software with human-level intelligence will have a transformative impact on the world. We’re dedicated to ensuring that that impact is a positive one.
We have enough funding to last for decades, and our backers include Y Combinator, researchers from OpenAI, Threshold, and a number of private individuals who care about effective altruism and scientific research.
Our research is focused primarily on self-supervised and generative video and audio models. We’re excited about opportunities to use simulated data, network architecture search, and good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.