C the Signs

Machine Learning Engineer

• Position SummaryThe Machine Learning Engineer will be responsible for the end-to-end development and deployment of Large language and machine learning models, with a primary focus on data preprocessing, model training, and fine-tuning using large-scale healthcare datasets. This role requires a strong understanding of Large language models, machine learning principles, data engineering, and experience working with sensitive healthcare data.
• Key Responsibilities
• - Data Preprocessing: Clean, transform, and prepare large, complex healthcare datasets for machine learning model development. This includes handling missing values, outlier detection, feature engineering, and data normalization. Identify, collect, and curate relevant, industry-specific datasets for model retraining. Format data appropriately for the chosen LLM and training pipeline
• - Model Training & Fine-Tuning: Design, train, and fine-tune various LLMs on extensive healthcare data to solve specific clinical or operational problems. Set up and manage the training environment, including GPU instances and required software. Train and fine-tune pre-trained LLMs on the custom dataset to achieve specific goals. Experiment with and fine-tune hyperparameters such as learning rate, batch size, and training epochs to optimize model performance. Integration of structured + unstructured data (multi-modal/multi-input models)
• - Model Evaluation & Optimization: Evaluate model performance using appropriate metrics, identify areas for improvement, and implement optimization strategies.
• - Pipeline Development: Develop and maintain robust and scalable data and ML pipelines for model training, inference, and deployment.
• - Collaboration: Work closely with data scientists, clinicians, and software engineers to understand requirements, integrate models into production systems, and ensure data privacy and security compliance.
• - Research & Development: Stay up-to-date with the latest advancements in machine learning and healthcare AI, and explore new technologies and methodologies to enhance our solutions.
• - Documentation: Maintain clear and comprehensive documentation of models, data pipelines, and experimental results.

Senior Machine Learning Engineer

Data Engineer

• Collaborate with data scientists and machine learning engineers to understand data requirements for LLM and machine learning model fine-tuning.
• Design, build, and maintain scalable data pipelines to ingest, process, and store massive and diverse healthcare datasets.
• Implement robust data validation and monitoring to ensure the integrity, accuracy, and consistency of all training datasets.
• Implement robust data cleaning, validation, and transformation processes to ensure data quality and integrity.
• Develop and optimize data structures and schemas for efficient access and utilization by LLMs and machine learning models.
• Work with the team to identify and acquire new data sources, ensuring compliance with relevant healthcare regulations (e.g., HIPAA).
• Monitor data pipeline performance, troubleshoot issues, and implement optimizations to improve efficiency and reliability.
• Document data engineering processes, data models, and data dictionaries.
• Stay up-to-date with the latest advancements in data engineering, big data technologies, and machine learning.

Senior Machine Learning Engineer

AI Data Engineer