About the Role
We are looking for a Staff Engineer with AI expertise to lead the development of advanced language and voice technologies that transform how patients and providers interact. In this role, you will drive Fabric’s work in large language model (LLM) applications—such as retrieval-augmented generation (RAG), classification, and fine-tuning—as well as speech, natural language processing, and machine learning. This is a hands-on, high-impact position at the intersection of innovation and production engineering. You will work cross-functionally to build intelligent, responsive dialogue systems and infrastructure for Fabric’s conversational AI experiences across voice and digital channels.
As the Staff Engineer, you will
- Design, build, and optimize LLM applications (e.g., RAG, classification, summarization).
- Prototype and productionize ML (machine learning) and AI features in Python, integrating them with backend services.
- Partner with product and medical teams to develop appropriate safeguards and business constraints for AI outputs.
- Collaborate with engineering to develop APIs for LLM applications used by other product components.
- Create automated evaluations to measure the accuracy and performance of LLM-powered systems.
- Maintain and improve existing NLP and AI diagnosis production components.
- Develop analytics to monitor system performance and prioritize improvements.
- Deploy AI services end-to-end in cloud-native environments using AWS and Kubernetes.
- Stay on the cutting edge by researching and testing new AI tools, APIs, and architectures.
- Contribute to our conversational AI strategy and help shape the future of healthcare AI.
Requirements
- 8+ years of experience in software engineering or applied machine learning, with a strong focus on building real-world AI/ML systems.
- Proficiency in backend development using Python (Flask or FastAPI).
- 3+ years of hands-on experience with LLMs and LLM agents.
- Solid understanding of embeddings and embedding databases.
- Experience in NLP or speech processing technologies.
- Familiarity with modern AI/ML frameworks and tools (e.g., Hugging Face, OpenAI API, LangChain, LangGraph).
- Experience building and deploying cloud-native applications on AWS with Kubernetes and container tools.
- Demonstrated ability to bring models from research to production, solving for latency, scale, and reliability.
- Effective communicator with the ability to work across disciplines in a fast-paced, and agile environment.
- You will likely to thrive in this role if you:
- Care deeply about deploying technology that empowers patients to engage with healthcare in their preferred language and format.
- Stay current on machine learning, foundation models, and algorithms related to text and text-to-speech technologies.
- Build robust testing and monitoring pipelines that provide insight into real-time performance and develop safeguards for responsible AI use.
- Work collaboratively across teams, integrating AI use cases into products by understanding their APIs and data systems.
- Operate with autonomy and ownership, focused on achieving meaningful outcomes.
- Excel at breaking down complex problems in the AI space and finding effective solutions.
- Communicate clearly with both technical and non-technical audiences.
Bonus Points
- Experience with conversational agents, or real-time voice communication (e.g., WebRTC, telephony), ASR, TTS, or voice assistants.
- Prior work on multimodal AI interfaces or agent-based dialogue systems.
- Experience hosting, scaling, and fine-tuning open-source models.
- A passionate interest in improving healthcare access and outcomes through applied AI.
Learn more about Fabric
At Fabric, we believe that a diverse workforce is essential to our success. We are an equal opportunity employer and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, color, religion, sex, national origin, age, disability, veteran status, or any other legally protected characteristic. We actively encourage individuals from all backgrounds to apply.