Machine Learning Engineer, Sr. - Remote Work

Our client is seeking a Senior Machine Learning Engineer to build and deploy AI products that help accelerate clinical trials and improve patient outcomes. This role focuses on turning advanced machine learning research into scalable, reliable production solutions, including machine learning models, LLM-powered agents, and AI knowledge base architecture.

You will work across the full machine learning lifecycle, from data ingestion and model development to deployment, monitoring, and scaling. Collaborating with data scientists, product managers, designers, engineers, and domain experts, you will help integrate AI capabilities into clinical research workflows. This is a remote role suited to an experienced engineer who combines strong technical judgment with analytical thinking, effective communication, and the ability to work independently., * Build and deploy AI-driven products that address real-world clinical research challenges and support improved patient outcomes.

• Develop machine learning models and LLM-powered agents for patient recruitment, enrollment forecasting, and study feasibility.
• Contribute to the expansion of AI knowledge base architecture to support new product capabilities.
• Build and maintain scalable data pipelines using modern cloud technologies and MLOps practices.
• Use Python, Clojure where applicable, AWS services such as Athena, Bedrock, and SageMaker, dbt, and Prefect to develop and deploy machine learning solutions.
• Implement CI/CD automation, testing, monitoring, and other practices that help keep models reliable and up to date.
• Integrate AI capabilities into the platform, including Care Access products, to support clinical research workflows.
• Collaborate with data scientists, product managers, designers, engineers, and domain experts to develop and deliver effective AI solutions.
• Stay current with developments in machine learning and artificial intelligence, including LLMs, natural language processing, and probabilistic modeling.
• Evaluate emerging techniques, experiment with new approaches, and turn promising prototypes into production-ready features.
• Monitor and improve model performance, scalability, reliability, and maintainability throughout the machine learning lifecycle.

• At least 5 years of hands-on experience building and deploying machine learning solutions in production at scale.
• Required healthcare experience, including an understanding of regulated environments, compliance requirements, security considerations, model governance, and bias and fairness concerns.
• Proven experience implementing end-to-end machine learning pipelines, from data ingestion through model serving, for real-world applications.
• Proficiency in Python and its machine learning ecosystem, including tools such as pandas, scikit-learn, TensorFlow, or PyTorch.
• Experience working with large datasets, writing complex SQL queries, and using modern data processing frameworks.
• Experience with AWS and containerization tools such as Docker.
• Familiarity with MLOps practices, including CI/CD pipelines, automated testing, and monitoring model performance and data drift.
• Strong understanding of machine learning fundamentals, including model selection, training, evaluation, feature engineering, and statistical modeling.
• Knowledge of natural language processing and large language models.
• Strong analytical and problem-solving skills, with the ability to develop efficient, effective solutions to complex problems.
• Ability to balance scientific rigor with practical engineering considerations to deliver accurate, performant, and maintainable production models.
• Clean and efficient coding practices, along with the ability to collaborate effectively and work independently in a remote environment.

Preferred Qualifications

• Experience with functional programming languages such as Clojure.
• Experience developing LLM-powered agents and applying advanced AI techniques to production use cases.
• Familiarity with probabilistic modeling and emerging machine learning approaches.

Our client develops technology solutions designed to support clinical research and help accelerate the development of new therapies. The team focuses on applying artificial intelligence and machine learning to real-world clinical research challenges, including patient recruitment, enrollment forecasting, and study feasibility. Its AI capabilities also support Care Access products and clinical research workflows.