Remote Mid Level Engineer - Machine Learning (NLP Focus)

We are seeking a highly skilled Senior Software Engineer specializing in Machine Learning, with a focus on Natural Language Processing (NLP), to join our dynamic team. The ideal candidate will play a pivotal role in understanding business objectives and leveraging machine learning models to meet these goals effectively. This position requires a blend of expertise in data science and software engineering, along with a passion for staying at the forefront of machine learning advancements.

If you are passionate about pushing the boundaries of machine learning and NLP to solve complex problems and drive innovation, we would love to hear from you. Apply now to contribute to our mission of leveraging cutting-edge technology to make a tangible impact.

Responsibilitie

• sModel Development: Understand business objectives and develop machine learning models to achieve these goals, complete with performance tracking metrics
• .Data Management: Ensure data quality through rigorous verification and cleaning processes. Explore and visualize data to understand it thoroughly, identifying any distribution differences that could impact model performance in real-world applications
• .Research and Data Acquisition: Proactively find and utilize available datasets online for model training purposes
• .Strategy and Validation: Define and implement robust validation strategies for model evaluation
• .Model Training and Tuning: Take charge of training models and fine-tuning hyperparameters to optimize performance
• .Model Deployment: Skillfully deploy models to production environments, ensuring seamless integration and operational efficiency
• .Industry Awareness: Maintain an up-to-date understanding of the latest developments in the machine learning field, with a keen eye on advancements in NLP

.Skills & Qualification

• sEducational Background: A Master’s degree in Machine Learning, Computer Science, or a related field, with a preference for specialization in the NLP domain
• .ML Experimentation: A solid understanding of setting up machine learning experiments, communicating results, and managing stakeholder expectations
• .Data Quality Management: Experience in verifying and ensuring data quality through comprehensive data cleaning processes
• .Model Development: Proven experience in training custom models with available data and conducting rapid experimentation for proof-of-concept projects
• .Technical Proficiency: Hands-on knowledge of at least one major machine learning framework, with a preference for PyTorch
• .MLOps Knowledge: Familiarity with MLOps practices, including model deployment and offering machine learning models as a service, is highly desirable
• .LLMs and Third-Party Services: Understanding of Large Language Models (LLMs) and third-party services, with the ability to evaluate the benefits of using these over in-house model development