We are looking for individuals who are passionate about
pushing the boundaries of computer vision
while also delivering efficient, production-ready models. Candidates with a strong record of open-source contributions or a demonstrable portfolio of impactful ML projects will stand out.
Key Responsibilities
- Design, implement, and optimize image/video ML models for large-scale generative AI applications
- Apply advanced techniques such as quantization, pruning, fine-tuning, and model distillation to improve efficiency and performance
- Work with deployment frameworks (ONNX, TensorRT, CUDA) to enable scalable inference
- Collaborate with researchers to bridge state-of-the-art techniques with production-grade engineering
- Conduct experiments, benchmark models, and document findings
- Share insights and collaborate with the broader ML research/engineering team
Required Qualifications
- Bachelor's, Master's, or PhD in Computer Science, AI, Machine Learning, or related fields
- Hands-on experience with deep learning frameworks: PyTorch (preferred), TensorFlow, or JAX
- Strong background in optimization techniques (quantization, pruning, distillation, mixed precision)
- Experience with deployment toolkits: ONNX, TensorRT, CUDA, or similar
- Demonstrated work in image/video model training, fine-tuning, or adaptation
- Proficiency in Python and familiarity with large-scale GPU-based training
Preferred Qualifications
- Experience with GGUF, ComfyUI, or other model deployment ecosystems
- Contributions to open-source computer vision or ML frameworks
- Research or industry experience with foundational/generative models (diffusion, transformers, multimodal)
- Familiarity with scaling ML models in distributed systems
Portfolio / Contribution Requirement
- Please share your GitHub profile showcasing open-source work in ML/Computer Vision
- If unavailable, provide a portfolio of projects, research papers, or documented achievements in model development, training, or optimization
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