Description:

The Deep Learning Engineer (The Neural Network Navigator) will design, train, and deploy deep learning models that unlock the potential of AI.
This role involves working with large datasets and implementing state-of-the-art algorithms to solve problems in computer vision, natural language processing (NLP), and speech recognition.
Key responsibilities include developing and implementing deep learning models using frameworks such as TensorFlow, PyTorch, or Keras for tasks like image classification and object detection.
The engineer will experiment with different neural network architectures and hyperparameters to optimize model performance using techniques like regularization and dropout.
Collaboration with data engineers and scientists is essential for collecting, preprocessing, and cleaning large datasets for training.
The role requires deploying trained models into production environments and ensuring they are scalable and efficient using cloud platforms like AWS, GCP, or Azure.
Continuous monitoring of model performance in production is necessary, along with retraining or fine-tuning as needed.
The engineer will work with cross-functional teams to integrate models into applications and ensure alignment with business goals.
Staying current with advancements in deep learning, computer vision, and NLP is crucial, including experimenting with techniques like transformers and reinforcement learning.

Requirements:

Extensive experience with deep learning algorithms such as CNNs, RNNs, transformers, GANs, VAEs, and reinforcement learning is required.
Proficiency in Python and experience with deep learning frameworks like TensorFlow, PyTorch, or Keras is necessary.
Experience in preprocessing and augmenting large datasets for deep learning tasks, particularly in computer vision and NLP, is essential.
Hands-on experience deploying deep learning models to cloud platforms such as AWS, Google Cloud, or Azure is required, along with familiarity with tools like Docker and Kubernetes.
A strong understanding of mathematical concepts behind deep learning, including linear algebra, probability, optimization, and gradient descent, is needed.
A Bachelor’s or Master’s degree in Computer Science, Machine Learning, AI, Data Science, or a related field is required, with equivalent experience also valued.
A minimum of 3 years of experience in deep learning engineering or machine learning is necessary, with a proven track record in developing, training, and deploying models in production.
Experience with cloud-based deep learning services and frameworks is highly desirable.

Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums are provided.
Competitive vacation, sick leave, and 20 paid holidays per year are included in the paid time off policy.
Flexible work schedules and telecommuting options promote work-life balance.
Opportunities for training, certification reimbursement, and career advancement programs are available for professional development.
Access to wellness programs, including gym memberships, health screenings, and mental health resources, is offered.
Life insurance and short-term/long-term disability coverage are part of the benefits package.
Confidential counseling and support services are available through the Employee Assistance Program (EAP).
Financial assistance for continuing education and professional development is provided through tuition reimbursement.
Opportunities to participate in community service and volunteer activities are encouraged.
Employee recognition programs celebrate achievements and milestones within the organization.