Unreal Gigs
Deep Learning Engineer (The Neural Network Navigator)
Unreal Gigs, San Francisco, California, United States, 94199
Are you a deep learning expert who thrives on designing and building neural networks that push the boundaries of artificial intelligence? Do you enjoy applying advanced deep learning techniques to real-world problems and building systems that deliver impactful results? If you’re passionate about creating scalable deep learning models that drive innovation, then
our client
has the perfect opportunity for you. We’re looking for a
Deep Learning Engineer
(aka The Neural Network Navigator) to design, train, and deploy deep learning models that unlock the potential of AI.As a Deep Learning Engineer at
our client , you’ll 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. You’ll be at the cutting edge of AI, helping to create intelligent systems that drive our products and solutions forward.Key Responsibilities:Design and Train Deep Learning Models:Develop and implement deep learning models using frameworks such as TensorFlow, PyTorch, or Keras. You’ll design architectures for tasks like image classification, object detection, NLP, and generative models (GANs, VAEs).Model Optimization and Tuning:Experiment with different neural network architectures and hyperparameters to optimize model performance. You’ll use techniques like regularization, dropout, and batch normalization to improve model accuracy and efficiency.Data Collection and Preprocessing:Work closely with data engineers and scientists to collect, preprocess, and clean large datasets for training deep learning models. You’ll implement data augmentation and other techniques to maximize the effectiveness of training data.Deploy and Scale Models:Deploy trained deep learning models into production environments, ensuring they are scalable and efficient. You’ll work with cloud platforms (e.g., AWS, GCP, Azure) to deploy models and optimize them for real-time inference.Model Monitoring and Maintenance:Continuously monitor the performance of models in production and retrain or fine-tune them as needed. You’ll ensure that models maintain high accuracy and adapt to new data over time.Collaboration with Cross-Functional Teams:Collaborate with product managers, software engineers, and data scientists to integrate deep learning models into applications and products. You’ll ensure that models align with business goals and deliver actionable insights.Stay Current with Deep Learning Advancements:Keep up-to-date with the latest research and advancements in deep learning, computer vision, and NLP. You’ll experiment with cutting-edge techniques like transformers, reinforcement learning, and unsupervised learning, and bring new ideas to the team.Required Skills: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. You’re comfortable designing, training, and deploying models from scratch.Programming Skills:
Proficiency in Python and experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. You can write clean, efficient code and implement custom layers or architectures when necessary.Data Processing and Augmentation:
Experience in preprocessing and augmenting large datasets for deep learning tasks, especially in areas like computer vision and NLP. You have expertise in data pipelines and transformation techniques.Cloud and Deployment Experience:
Hands-on experience deploying deep learning models to cloud platforms such as AWS, Google Cloud, or Azure, using tools like Docker, Kubernetes, and TensorFlow Serving.Mathematical and Statistical Foundations:
Strong understanding of the mathematical concepts behind deep learning, such as linear algebra, probability, optimization, and gradient descent.Educational Requirements:Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, Data Science, or a related field.
Equivalent experience in deep learning engineering is also highly valued.Certifications or additional coursework in deep learning, AI, or related fields are a plus.Experience Requirements:3+ years of experience in deep learning engineering or machine learning,
with a proven track record of developing, training, and deploying deep learning models in production environments.Experience working with large datasets, developing complex neural network architectures, and optimizing models for performance and scalability.Experience with cloud-based deep learning services and frameworks (e.g., AWS SageMaker, Google AI Platform) is highly desirable.
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our client
has the perfect opportunity for you. We’re looking for a
Deep Learning Engineer
(aka The Neural Network Navigator) to design, train, and deploy deep learning models that unlock the potential of AI.As a Deep Learning Engineer at
our client , you’ll 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. You’ll be at the cutting edge of AI, helping to create intelligent systems that drive our products and solutions forward.Key Responsibilities:Design and Train Deep Learning Models:Develop and implement deep learning models using frameworks such as TensorFlow, PyTorch, or Keras. You’ll design architectures for tasks like image classification, object detection, NLP, and generative models (GANs, VAEs).Model Optimization and Tuning:Experiment with different neural network architectures and hyperparameters to optimize model performance. You’ll use techniques like regularization, dropout, and batch normalization to improve model accuracy and efficiency.Data Collection and Preprocessing:Work closely with data engineers and scientists to collect, preprocess, and clean large datasets for training deep learning models. You’ll implement data augmentation and other techniques to maximize the effectiveness of training data.Deploy and Scale Models:Deploy trained deep learning models into production environments, ensuring they are scalable and efficient. You’ll work with cloud platforms (e.g., AWS, GCP, Azure) to deploy models and optimize them for real-time inference.Model Monitoring and Maintenance:Continuously monitor the performance of models in production and retrain or fine-tune them as needed. You’ll ensure that models maintain high accuracy and adapt to new data over time.Collaboration with Cross-Functional Teams:Collaborate with product managers, software engineers, and data scientists to integrate deep learning models into applications and products. You’ll ensure that models align with business goals and deliver actionable insights.Stay Current with Deep Learning Advancements:Keep up-to-date with the latest research and advancements in deep learning, computer vision, and NLP. You’ll experiment with cutting-edge techniques like transformers, reinforcement learning, and unsupervised learning, and bring new ideas to the team.Required Skills: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. You’re comfortable designing, training, and deploying models from scratch.Programming Skills:
Proficiency in Python and experience with deep learning frameworks like TensorFlow, PyTorch, or Keras. You can write clean, efficient code and implement custom layers or architectures when necessary.Data Processing and Augmentation:
Experience in preprocessing and augmenting large datasets for deep learning tasks, especially in areas like computer vision and NLP. You have expertise in data pipelines and transformation techniques.Cloud and Deployment Experience:
Hands-on experience deploying deep learning models to cloud platforms such as AWS, Google Cloud, or Azure, using tools like Docker, Kubernetes, and TensorFlow Serving.Mathematical and Statistical Foundations:
Strong understanding of the mathematical concepts behind deep learning, such as linear algebra, probability, optimization, and gradient descent.Educational Requirements:Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, Data Science, or a related field.
Equivalent experience in deep learning engineering is also highly valued.Certifications or additional coursework in deep learning, AI, or related fields are a plus.Experience Requirements:3+ years of experience in deep learning engineering or machine learning,
with a proven track record of developing, training, and deploying deep learning models in production environments.Experience working with large datasets, developing complex neural network architectures, and optimizing models for performance and scalability.Experience with cloud-based deep learning services and frameworks (e.g., AWS SageMaker, Google AI Platform) is highly desirable.
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