Mechine Learning Engineer
3 weeks ago
About Matrice: Matrice AI is building a one-stop solution to build, deploy and manage AI applications. We help customers build applications with low or no code.
Responsibilities:
- Develop and implement state-of-the-art computer vision models for image segmentation, object detection, and video classification
- Design and conduct experiments to improve model performance and efficiency
- Implement action tracking algorithms for video processing applications
- Collaborate with the backend team to integrate ML models into the production environment
- Preprocess and analyze large datasets for training computer vision models
- Optimize model performance for deployment on various hardware platforms
- Stay up-to-date with the latest advancements in computer vision and deep learning
- Contribute to the development of ML pipelines for model training, evaluation, and deployment
- Participate in code reviews and contribute to improving ML development practices
Qualifications:
- Final year Computer Science graduate (BE/BTech/ME/MTech)
- Good knowledge of Operating Systems, Databases, and Networking
- Previous industry experience in using Deep Learning algorithms for Computer Vision, especially in video processing
- Familiarity with current SOTA computer vision models, including fundamental models like Segment Anything and CLIP
- Completed deep learning courses from Coursera or DeepLearning.ai
- Strong understanding of Deep Learning model training on Computer Vision datasets
- Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow
- Strong fundamental knowledge of algorithms and data structures
- Excellent troubleshooting and problem-solving skills
- Learning mentality and openness to new technologies
- Basic understanding of MongoDB, Git, and Docker Containerization (preferred)
- Knowledge in MERN stack or any other backend stack (preferred)
Why Join Matrice?
Join Matrice AI to work on cutting-edge AI and computer vision applications, gain valuable industry experience in machine learning, and be part of a team building innovative low-code/no-code solutions.
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