Key skills
Natural Language ProcessingMachine LearningPythonDeep Learning FrameworksHugging FaceDatabricks/SparkCloud PlatformsSQLBusiness CommunicationNoSQLAIMLDeep LearningNLPGenerative AILarge Language ModelsRAGLangChainPyTorchTensorFlowMLOpsMLflowData EngineeringDatabricksSparkAWSKubernetesDockerSageMakerCI/CD
About this role
Careforth is a pioneer in the caregiving space, supporting family caregivers across the United States. The Senior Machine Learning Engineer will lead the design and deployment of advanced Natural Language Processing solutions to enhance caregiver support, transforming unstructured data into actionable insights.
Responsibilities:
- Design and implement scalable NLP architectures, including Transformers, BERT-based models, and Generative AI frameworks tailored for healthcare data
- Build robust preprocessing pipelines for unstructured text to perform tokenization, Named Entity Recognition (NER), and sentiment analysis
- Fine-tune Large Language Models (LLMs) and apply quantization techniques to optimize performance in live production environments
- Partner with Data Engineering to build automated labeling workflows and feature stores that support Medallion Architecture standards
- Lead the adoption of MLOps best practices using MLflow, Docker, and Kubernetes to ensure seamless CI/CD for ML pipelines
- Monitor production model health to mitigate data drift and algorithmic bias, ensuring all outputs meet clinical safety and compliance standards
- Influence the strategic roadmap by evaluating emerging technologies (e.g. Vector Databases) and mentor junior engineers on experimental design and model architecture
- Perform other duties and special projects as assigned
Requirements:
- MS/Advanced degree in Computer Science, Computational Linguistics, or a related quantitative field
- 7-10 years in Machine Learning, with a significant history of deploying large-scale systems in production
- Expert-level proficiency in Hugging Face, LangChain, spaCy, and RAG-based architectures
- Master-level Python and deep learning framework expertise (PyTorch/TensorFlow)
- Extensive experience with Databricks/Spark (PySpark) and cloud platforms such as AWS SageMaker
- Demonstrated ability to research and learn new technical tools while analyzing data across multiple complex systems
- Demonstrated ability to manipulate data across multiple systems, including SQL, SAS, and NoSQL environments
- Exceptional ability to translate complex algorithmic results into clear Business Value for non-technical executive stakeholders