Checking job availability...
Original
Simplified
Job Title : Machine Learning
Location : Remote
Duration : Long Term contract
Job Description:
Bachelor's or higher degree in Computer Science, Machine Learning, or a related field.
Proven experience in maintaining and enhancing machine learning systems, preferably in document processing.
Strong proficiency in Python and relevant machine learning libraries/frameworks like TensorFlow, PyTorch.
Proven expertise in working with Image Transformer models, particularly those designed for document image understanding like Microsoft's DiT (Document Image Transformer).
Demonstrated experience in implementing and working with self-supervised learning techniques, especially in the context of pre-training models on large-scale unlabeled text images. Familiarity with approaches like DiT for Document AI tasks.
Need ML Engineer (Microsoft DIT)-Initially Remote After Few Months Hybrid
Location: Washington DC - Initially Remote After Few Months Hybrid (Once or Twice in a month)
Hands-on experience in applying Transformer models to various Document AI tasks, including document image classification and document layout analysis.
Proven ability to leverage self-supervised pre-trained models, like DiT, as backbone networks to achieve state-of-the-art results in downstream tasks.
Proficient in designing experiments, analyzing results, and fine-tuning models to achieve optimal performance on Document AI tasks. Ability to interpret and communicate experiment results effectively.
Familiarity with integrating Transformer models into OCR pipelines and collaborating with OCR technologies for enhanced text detection and extraction capabilities.
Experience with text feature extraction techniques like TF-IDF for document text processing and analysis.
Solid understanding of image processing techniques with a focus on leveraging OpenCV for tasks such as resizing, feature extraction, and other pre-processing steps essential for document image analysis and understanding.
Experience with AWS SageMaker Studio, including model training, deployment, and monitoring within the SageMaker ecosystem.
Familiarity with AWS AutoML services like SageMaker Autopilot to streamline ML model development and deployment.
Experience with AutoML frameworks such as AutoGluon for automated model selection, hyperparameter tuning, and optimizing machine learning pipelines.
Familiarity with cloud platforms (e.g., AWS, Azure) and containerization (e.g., Docker).
Excellent problem-solving skills and ability to work independently.
Strong communication skills and ability to collaborate with cross-functional teams.
Responsibilities
Maintain and optimize the existing document code prediction system.
Collaborate with cross-functional teams to understand business requirements and implement enhancements.
Monitor system performance and troubleshoot issues as they arise.
Stay updated on the latest advancements in machine learning and implement improvements to the prediction model.
Explore and identify new machine learning opportunities within the document processing domain.
Utilize AWS SageMaker Studio for model development, training, and deployment to improve system efficiency.
Leverage AutoML techniques such as AutoGluon and AWS AutoML to streamline model optimization, hyperparameter tuning, and automation of ML workflows.
Implement AutoML solutions to enhance Document AI model accuracy and performance with minimal manual intervention.
Apply TF-IDF techniques for text-based feature extraction to improve document classification and NLP-based document processing models.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
Report this job Job Title : Machine Learning
Location : Remote
Duration : Long Term contract
Job Description:
Bachelor's or higher degree in Computer Science, Machine Learning, or a related field.
Proven experience in maintaining and enhancing machine learning systems, preferably in document processing.
Strong proficiency in Python and relevant machine learning libraries/frameworks like TensorFlow, PyTorch.
Proven expertise in working with Image Transformer models, particularly those designed for document image understanding like Microsoft's DiT (Document Image Transformer).
Demonstrated experience in implementing and working with self-supervised learning techniques, especially in the context of pre-training models on large-scale unlabeled text images. Familiarity with approaches like DiT for Document AI tasks.
Need ML Engineer (Microsoft DIT)-Initially Remote After Few Months Hybrid
Location: Washington DC - Initially Remote After Few Months Hybrid (Once or Twice in a month)
Hands-on experience in applying Transformer models to various Document AI tasks, including document image classification and document layout analysis.
Proven ability to leverage self-supervised pre-trained models, like DiT, as backbone networks to achieve state-of-the-art results in downstream tasks.
Proficient in designing experiments, analyzing results, and fine-tuning models to achieve optimal performance on Document AI tasks. Ability to interpret and communicate experiment results effectively.
Familiarity with integrating Transformer models into OCR pipelines and collaborating with OCR technologies for enhanced text detection and extraction capabilities.
Experience with text feature extraction techniques like TF-IDF for document text processing and analysis.
Solid understanding of image processing techniques with a focus on leveraging OpenCV for tasks such as resizing, feature extraction, and other pre-processing steps essential for document image analysis and understanding.
Experience with AWS SageMaker Studio, including model training, deployment, and monitoring within the SageMaker ecosystem.
Familiarity with AWS AutoML services like SageMaker Autopilot to streamline ML model development and deployment.
Experience with AutoML frameworks such as AutoGluon for automated model selection, hyperparameter tuning, and optimizing machine learning pipelines.
Familiarity with cloud platforms (e.g., AWS, Azure) and containerization (e.g., Docker).
Excellent problem-solving skills and ability to work independently.
Strong communication skills and ability to collaborate with cross-functional teams.
Responsibilities
Maintain and optimize the existing document code prediction system.
Collaborate with cross-functional teams to understand business requirements and implement enhancements.
Monitor system performance and troubleshoot issues as they arise.
Stay updated on the latest advancements in machine learning and implement improvements to the prediction model.
Explore and identify new machine learning opportunities within the document processing domain.
Utilize AWS SageMaker Studio for model development, training, and deployment to improve system efficiency.
Leverage AutoML techniques such as AutoGluon and AWS AutoML to streamline model optimization, hyperparameter tuning, and automation of ML workflows.
Implement AutoML solutions to enhance Document AI model accuracy and performance with minimal manual intervention.
Apply TF-IDF techniques for text-based feature extraction to improve document classification and NLP-based document processing models.
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
Report this job