Any-to-Any AI Research Engineer - Ω Labs
Building the Future of Any-to-Any AI Models
About OMEGA Labs
At OMEGA Labs, we're pushing the boundaries of multimodal AI through our groundbreaking work on any-to-any models. Operating one of the highest-performing subnets on Bittensor (Subnet 21), we're a well-funded, revenue-positive AI lab with significant resources for research and development. Previously, we were the AI brains behind WOMBO (200M+ downloads, 2B+ generated pieces of content), and now we're taking on an even bigger challenge: creating truly universal multimodal models that can understand, reason about, and generate across all modalities.
The Challenge
We're developing next-generation any-to-any models that will:
- Process and generate across text, image, audio, and video modalities
- Power the future of human-AI interaction through OMEGA Focus
- Run on decentralized infrastructure via Bittensor
- Push the boundaries of open-source AI capabilities
What We're Looking For
We need exceptional AI researchers who are:
- Architecture Innovators: Capable of designing and implementing novel multimodal architectures
- Experimental Pioneers: Thrive on rapid prototyping of new ideas from latest research papers
- Data Wizards: Experienced with multimodal datasets and training pipelines
- Open Source Champions: Passionate about contributing to and building on open-source AI
Technical Requirements
- Strong background in:
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Multimodal transformers and fusion techniques
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Speech processing and generation
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Distributed training systems
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PyTorch and modern AI frameworks
- Experience with:
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Large-scale model training
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Model optimization and quantization
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Real-world deployment of AI systems
Current Technical Challenges
- Implementing early fusion transformer architectures
- Developing speech2speech capabilities
- Creating efficient multimodal tokenization methods
- Building distributed training infrastructure
- Designing novel evaluation metrics for multimodal models
Our Culture
- Rapid experimentation and iteration
- Research freedom with real-world impact
- Focus on shipping and practical results
- Lean team of exceptional engineers
Why Join Us
- Work on cutting-edge multimodal AI with real applications
- Access to significant compute resources
- Early-stage equity in a rapidly growing, revenue-positive AI lab
- Highly competitive compensation packages
- Flexible, remote-first environment
- Opportunity to shape the future of AI
The Stack
- PyTorch
- Bittensor Network
- Llama 3, ImageBind
- Custom multimodal architectures
- Distributed training infrastructure
Challenge Question
Design an architecture that can efficiently:
1. Process multiple modalities simultaneously
2. Generate high-quality outputs across modalities
3. Scale effectively in a distributed setting
Include your thoughts on this with your application!
Apply
Send your:
- Resume
- GitHub profile/publications
- Response to the challenge question
- Brief note about what excites you about any-to-any models