Deep Learning Solutions Engineer

3 weeks ago


San Francisco, California, United States Unreal Gigs Full time
Unlock AI Potential with Unreal Gigs

Earn an estimated $160,000 - $220,000 per year as a Deep Learning Engineer at Unreal Gigs.

In this role, you will work on designing and training deep learning models to unlock the potential of AI. You will have the opportunity to work with large datasets, design complex neural networks, and implement state-of-the-art algorithms to solve challenging problems in areas such as computer vision, natural language processing (NLP), and speech recognition.

With your extensive experience in deep learning algorithms and proficiency in Python, you will develop and implement deep learning models using frameworks such as TensorFlow, PyTorch, or Keras. Your expertise in data processing and augmentation, cloud and deployment experience, and strong understanding of mathematical concepts behind deep learning will be valuable assets in this position.

You will collaborate with cross-functional teams to integrate deep learning models into applications and products, and ensure that models align with business goals and deliver actionable insights.

Key Responsibilities:

  • Design and Train Deep Learning Models: Develop and implement deep learning models using frameworks such as TensorFlow, PyTorch, or Keras.
  • Model Optimization and Tuning: Experiment with different neural network architectures and hyperparameters to optimize model performance.
  • Data Collection and Preprocessing: Work closely with data engineers and scientists to collect, preprocess, and clean large datasets for training deep learning models.
  • Deploy and Scale Models: Deploy trained deep learning models into production environments, ensuring they are scalable and efficient.
  • Model Monitoring and Maintenance: Continuously monitor the performance of models in production and retrain or fine-tune them as needed.
  • Collaboration with Cross-Functional Teams: Collaborate with product managers, software engineers, and data scientists to integrate deep learning models into applications and products.
  • Stay Current with Deep Learning Advancements: Keep up-to-date with the latest research and advancements in deep learning, computer vision, and NLP.

Requirements:

  • Deep Learning Expertise: Extensive experience with deep learning algorithms such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, generative models (GANs, VAEs), and reinforcement learning.
  • Programming Skills: Proficiency in Python and experience with deep learning frameworks like TensorFlow, PyTorch, or Keras.
  • Data Processing and Augmentation: Experience in preprocessing and augmenting large datasets for deep learning tasks, especially in areas like computer vision and NLP.
  • Cloud and Deployment Experience: Hands-on experience deploying deep learning models to cloud platforms such as AWS, Google Cloud, or Azure.


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