Genmo

Genmo

Research Engineer (New Grad)

San Francisco HQ

Sponsorship not specifiedDetected 22 hours ago
PythonMachine LearningDeep LearningTensorFlowPyTorchNLPComputer VisionLLMsResearchCommunicationCollaboration

About the role

  • We're seeking an exceptional Software Engineer to join our research team in advancing the frontiers of visual generative AI.

Responsibilities

  • Implement and experiment with various generative architectures (Diffusion Models, GANs, Transformers)
  • Help optimize model performance and scaling capabilities
  • Collaborate with cross-functional teams to integrate research innovations
  • As a Research Engineer, you'll work alongside experienced researchers to develop and improve generative models while ensuring their safe and effective deployment.

Requirements

  • Clear communication skills and ability to work in a research team

Nice to have

  • Research projects or thesis work in generative AI
  • Experience with different types of generative models:
  • Diffusion Models
  • Transformer architectures
  • Large Language Models
  • Familiarity with distributed computing and GPU optimization
  • Contributions to open-source ML projects
  • Academic publications or workshop papers

Benefits

  • Contribute to the development of novel machine learning techniques for visual generative modeling
  • BS or MS in Computer Science, Machine Learning, or related field (recent graduates welcome)
  • 2+ years of experience in Machine Learning
  • Strong foundation in machine learning, particularly generative models
  • Experience with deep learning frameworks and model training
  • Understanding of fundamental concepts in computer vision and generative AI

Company info

  • We are Genmo, a research lab dedicated to building open, state-of-the-art models for video generation towards unlocking the right brain of AGI.
  • Join us in shaping the future of AI and pushing the boundaries of what's possible in video generation.

This listing is sourced directly from Genmo's careers page and normalized into a canonical job model.