Summary
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.
Example projects
• 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.
You are
• 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.
Benefits
• 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
About us
• 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 a good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.
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.
Example projects
• 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.
You are
• 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.
Benefits
• 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
About us
• 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 a good theoretical understanding of deep learning to make progress on these problems. We take a focused, engineering-driven approach to research.