About Us A pioneer in the caregiving space, Careforth supports family caregivers across the United States to confidently care for their loved ones at home. Through a combination of in-person home visits, remote coaching and our proprietary digital collaboration app, we provide caregivers with support, guidance, confidence, and connection to resources they need. The Caregivers and families we support stay with Careforth for many years, building lasting relationships along the way. Join us today and live our values: lead with heart, cultivate trust, go beyond. Position Summary The Senior Machine Learning Engineer will be a cornerstone of our AI/ML organization, spearheading the design and deployment of advanced Natural Language Processing (NLP) solutions to revolutionize caregiver support. You will transform unstructured caregiver notes and visit observations into production-grade AI models, specializing in Retrieval-Augmented Generation (RAG), document intelligence, and predictive care analytics. The ideal candidate brings engineering maturity and a track record of building end-to-end ML pipelines that turn complex text into actionable care insights. What You Will Do - 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 What You Will Bring - 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. You'll Benefit From At Careforth your well-being matters. With flexible schedules, a remote-first culture, and a nationally recognized wellness program, our benefits are designed to help you thrive, both professionally and personally. Discover how we invest in you: https://careforth.com/careers/#benefits The pay range for this position is $121,00 - $190000. The actual wage offered may be lower or higher depending on budget and candidate experience, knowledge, skills, qualifications, and geographic location. #Li-Remote- except for CA Join Our Award Winning Team Founded in Boston, Careforth's caregiver programs and services improve health outcomes, keeping care at home longer. Additionally, our programs provide financial benefit to caregivers and cost savings to state agencies and health plans. At Careforth, we understand the challenges of caregiving and are committed to supporting family caregivers at every turn. Caregivers play a critical role in the future of healthcare—and so can you. Apply now! For more information, please visit www.Careforth.com. Careforth is an Equal Opportunity Employer* DISCLAIMER: Please note this job description is not designed to cover or contain a comprehensive listing of activities, duties or responsibilities that are required of the employee for this job. *Careforth supports families with diverse backgrounds and as an equal opportunity employer, we seek employees who reflect the diverse population we serve. Careforth complies with all applicable laws concerning hiring and employment practices and is firmly committed to fostering and maintaining a workplace free from discrimination. We pledge to hire, train, and promote our employees without regard to race, religion, gender, gender identity, genetic information, age, national origin, sexual orientation, disability, veteran status, or any other category protected by applicable law. Careforth strives to create experiences that are accessible and welcoming to everyone, including making www.careforth.com and the careers site accessible to any and all users. If you would like to contact us regarding the company’s diversity, equity and inclusion initiatives, inquire about a specific accessibility need or the accessibility of our website, or if you need assistance completing an application process, please contact People & Culture at 866-797-2333.

• Support cross-functional teams in designing and implementing machine learning-based solutions • Contribute to Machine Learning Engineering (MLE) internal tools and best practices • Work with Engineers to integrate Data Science models into customer-facing solutions • Support model development through Google Cloud Platform (GCP) service enablement and configuration • Contribute to the roadmap for Data Science and MLE team tools and technology development • Design and implement monitoring and alerting systems to maintain model performance and integrity • Mentor and guide junior machine learning engineers, providing technical expertise and fostering a culture of continuous learning and development • Optimize and fine-tune models to achieve peak performance and accuracy within resource constraints

• Collaborate with AI leaders, data scientists, ML engineers, and product teams to build agentic frameworks • Assist in building and refining AI agents using LLMs and multimodal models for tasks such as data exploration, scenario planning, and workflow automation • Design prompts and workflows that enable agents to reason, plan, and act autonomously • Work with structured and unstructured datasets, metadata and metrics definitions to create a contextual layer that enables agent performance • Conduct rigorous testing of agent capabilities, including edge cases and failure modes • Maintain detailed records of prompts, responses, agent iterations, and performance metrics • Work closely with cross-functional teams to align ML solutions with business goals and deployment requirements • Build, train, and evaluate ML models for tasks such as classification, regression, clustering, and anomaly detection • Clean, transform, and analyze structured and unstructured datasets to support model training and validation • Optimize models for accuracy, efficiency, and scalability using techniques like hyperparameter tuning and cross-validation • Maintain clear records of experiments, results, and insights for reproducibility and team knowledge sharing

• Own the full ML lifecycle including feature pipelines, training workflows, model deployment, inference services, monitoring, and retraining • Design and build reliable data and feature pipelines • Build and optimize machine learning models including regression, classification, ranking, and recommender systems • Implement and manage batch scoring pipelines and online inference services • Partner with data scientists to operationalize models • Collaborate with software engineers for integration into production services • Establish observability for ML systems • Design systems for rapid experimentation and safe rollout of new models • Document architecture, establish best practices, and mentor teammates