Senior Machine Learning Engineer II
Capital RxRemotePosted 26 March 2026
Tech Stack
Job Description
About Judi Health
Judi Health is an enterprise health technology company providing a comprehensive suite of solutions for employers and health plans, including:
Capital Rx , a public benefit corporation delivering full-service pharmacy benefit management (PBM) solutions to self-insured employers,
Judi Health™ , which offers full-service health benefit management solutions to employers, TPAs, and health plans, and
Judi® , the industry’s leading proprietary Enterprise Health Platform (EHP), which consolidates all claim administration-related workflows in one scalable, secure platform.
Together with our clients, we’re rebuilding trust in healthcare in the U.S. and deploying the infrastructure we need for the care we deserve. To learn more, visit www.judi.health .
Position Summary:
Join our mission to infuse cutting-edge AI/ML/GenAI into pharmacy benefits as a Senior Machine Learning Engineer. We are looking for an experienced software engineer with machine learning expertise to join us in expanding our AI capabilities, enabling increased productivity and magical experiences in our products and services.
In this role you will be expected to design and implement complex AI systems that leverage ML models for NLP, NLG, multimodal data analysis, chatbots, and RAG-based QnA. The ideal candidate should be passionate about applying AI/ML concepts to difficult problems and develop scalable solutions. We want people who like working in a collaborative team environment and enjoy creating practical, efficient, and high-performance software that leverages Large Language Models (LLM), Multimodal Language Models(MLM), and other ML models and techniques to build amazing capabilities for our customers, partners, and employees. Most importantly, we are a mission-oriented, high-growth startup and we are looking for folks that are excited to be part of our journey to make lasting impact towards transforming healthcare.
Responsibilities:
Design, develop, and productionize machine learning (ML) solutions in the fields of Document understanding, Search and QnA, GenAI, Virtual Agents, etc.
Develop and maintain backend services using Python, focusing on AI-driven applications.
Design and implement APIs for seamless integration with AI models and services.
Collaborate with product managers, engineers, and other stakeholders as a specialist and subject matter expert in machine learning.
Work across the complete lifecycle of ML model development, including problem definition, data exploration, feature engineering, model training, validation, and deployment.
Participate in code reviews, testing, and quality assurance processes.
Troubleshoot and resolve technical issues related to AI model, integration, deployment and backend services.
Implement system telemetry, A/B testing and other statistical methods to validate the effectiveness of AI models. Ensure the integrity and robustness of ML solutions by developing automated testing and validation processes.
Document and communicate development processes, system designs, and architectural decisions.
Ensure the security and compliance of healthcare data, adhering to HIPAA regulations.
Responsible for adherence to the Capital Rx Code of Conduct, including
reporting of
noncompliance.
Required Qualifications:
Bachelor’s or Master’s degree in Computer Science, Machine Learning, or a related quantitative field.
Minimum 6+ years of experience in industry with a strong focus on ML solutions development and production deployment.
Strong grasp of OOP, Design Patterns, efficient algorithms, and quality software development.
Strong proficiency in Python and familiarity with ML libraries such as PyTorch.
Statistical Machine Learning and Artificial Intelligence in the context of NLP, Speech, RAG, Virtual Agents, and/or Document Processing technologies
Familiarity with cloud platforms such as AWS or Azure.
Familiarity with GenAI services such as OpenAI GPT, Anthropic Claude, Google Gemma etc. Ex ... (truncated, view full listing at source)
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