AI Research Scientist

4 weeks ago

Toronto, Ontario, Canada Unreal Gigs Full time
Unlock the Future of AI

Are you passionate about pushing the boundaries of artificial intelligence and machine learning? Do you thrive on developing cutting-edge deep learning models and conducting pioneering research that shapes the future of AI? If you're fascinated by the endless possibilities of deep learning and neural networks, then our client has the perfect opportunity for you.

As an AI Research Scientist at Unreal Gigs, you will work closely with AI scientists, machine learning engineers, and product teams to design novel deep learning algorithms, conduct experiments, and develop models that solve complex problems across various domains, from computer vision and natural language processing to generative models and reinforcement learning.

Key Responsibilities:
  1. Conduct Cutting-Edge Research in Deep Learning:
  • Design and develop innovative deep learning algorithms and architectures that push the limits of current AI capabilities. You'll experiment with state-of-the-art techniques, such as GANs, transformers, RNNs, CNNs, and self-supervised learning, to create models that solve complex tasks.
Explore Novel Architectures and Techniques:
  • Investigate new architectures and approaches, including convolutional networks (CNNs), recurrent networks (RNNs), transformers, and neural architecture search (NAS). You'll explore advanced techniques like meta-learning, few-shot learning, and unsupervised learning to advance the performance of deep learning models.
Develop Scalable Deep Learning Models:
  • Build and implement deep learning models that can handle large datasets, optimizing them for speed, accuracy, and scalability. You'll work with frameworks like TensorFlow, PyTorch, or JAX to create models that perform efficiently in production environments.
Conduct Experiments and Optimize Models:
  • Perform experiments to evaluate model performance, experimenting with hyperparameters, model architectures, and optimization strategies. You'll iterate on model development to improve accuracy, reduce bias, and ensure models generalize well to new data.
Collaborate with Cross-Functional Teams:
  • Work closely with product teams, AI engineers, and data scientists to translate deep learning research into real-world applications. You'll ensure that your models and algorithms align with business objectives and can be deployed in production environments.
Publish Research and Contribute to the AI Community:
  • Publish research findings in top-tier AI and machine learning conferences (NeurIPS, ICML, CVPR, etc.). You'll contribute to the AI community by sharing insights, participating in discussions, and advancing knowledge in the field of deep learning.
Stay Updated on AI and Deep Learning Advances:
  • Keep up-to-date with the latest advancements in deep learning, including developments in model architectures, optimization techniques, and novel algorithms. You'll continuously experiment with cutting-edge approaches to stay at the forefront of AI innovation.

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