# Senior AI/ML Engineer
We're looking for a Senior AI Engineer who is passionate about transforming healthcare through cutting-edge technology. In this role, you will make hands-on contributions to the design, development, and deployment of advanced Machine Learning (ML) and Natural Language Processing (NLP) solutions powering Patient Finder.
You'll work on high-impact initiatives including Generative AI, Named Entity Recognition and Linking (NER/NEL), multilingual NLP, and continuous learning on complex healthcare datasets. This is a unique opportunity to help define the future of AI in healthcare, with deep technical ownership and end-to-end responsibility for production-grade ML solutions.
## Key Responsibilities
- Design, build, and maintain robust, scalable ML and NLP solutions for production environments.
- Contribute hands-on to the codebase, implementing high-quality, production-ready AI systems.
- Collaborate closely with cross-functional engineering teams to ensure seamless integration of AI components with our search engine and data warehouse.
- Apply best practices in software engineering, including testing, observability, and CI/CD for ML systems.
- Support the internationalization and localization of NLP components across multiple languages.
- Work with the Product Owner and engineering stakeholders to translate business requirements into high-quality ML deliverables.
- Research, evaluate, and apply state-of-the-art NLP and ML methodologies.
- Share expertise with peers, contribute to knowledge-sharing sessions, and informally mentor junior engineers.
## Requirements
- 6+ years of industry experience in AI/NLP, with a strong track record of delivering production-grade solutions.
- Strong proficiency in Python for ML and NLP; experience with Go, Rust, or Java is a plus.
- Solid software engineering fundamentals and experience integrating ML systems into large-scale production environments.
- Deep expertise in supervised and unsupervised learning, deep learning, and NLP.
- Experience deploying, monitoring, and maintaining production ML/NLP systems.
- Ability to clearly communicate complex technical concepts to cross-functional teams.
- Strategic mindset for data science product planning under uncertainty.
- Holistic understanding of end-to-end ML pipelines within broader system architectures.
- Strong problem-solving and critical thinking skills.