Softthink Solutions Inc Hiring For Machine Learning Engineer at Remote

Softthink Solutions Inc; United States; Remote; USD 125,000 - 150,000; Full time

Job summary

An innovative firm is on the lookout for a talented Machine Learning Engineer to join their dynamic team. This exciting position involves scripting, developing, and validating machine learning models using cutting-edge platforms and libraries. You will play a crucial role in building and training models tailored to business needs while contributing to ongoing enhancements. If you are passionate about machine learning and eager to work with the latest technologies, this opportunity is perfect for you to make a significant impact in a forward-thinking environment.

Qualifications

• Proficiency in scripting and developing machine learning models using popular frameworks.
• Experience in hyperparameter tuning and model validation techniques.

Responsibilities

• Build and train machine learning models based on business requirements.
• Contribute to continuous improvement of model performance.

Skills

Machine Learning

Tensorflow

Keras

Pytorch

Scikit-Learn

Deep Learning

Neural Networks

Hyperparameter Tuning

Model Validation

Docker

Azure Machine Learning

Tools

Docker

Azure Machine Learning Pipelines

Job description

Job Description

Description: We are seeking a skilled Machine Learning Engineer to script, develop, and validate machine learning models using cutting-edge platforms and libraries. Your role will involve building and training models based on business requirements and contributing to continuous improvement.

Technical Requirements:

1. Proficiency in scripting and developing machine learning models using popular frameworks such as Tensorflow, Keras, Pytorch, or Scikit-Learn.
2. Knowledge of deep learning architectures, neural networks, and transfer learning for model development.
3. Experience in hyperparameter tuning and model validation techniques to ensure model accuracy.
4. Familiarity with Docker containers and container orchestration tools for deploying ML models.
5. Understanding of Azure Machine Learning Pipelines for efficient model deployment and management.
6. Knowledge of model monitoring tools and techniques for performance evaluation over time.
7. Continuous learning and adaptation to stay updated with the latest advancements in machine learning.