Machine Learning Engineer / Researcher (Computer Vision – Optimization & Quantization)

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.