Senior Software Engineer, Artificial Intelligence

Fabric

49d•$150,000 - $190,000

About The Position

We are looking for a Senior 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 (NLP), and machine learning (ML). 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.

Requirements

A bachelor's degree in a related field is preferred, but not required.
5+ 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), and 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 communication skills and the ability to work across disciplines in a fast-paced, agile environment.

Nice To Haves

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.

Responsibilities

Designing, building, and optimizing LLM applications (e.g., RAG, classification, summarization).
Prototyping and productionizing ML and AI features in Python, integrating them with backend services.
Partnering with product and medical teams to develop appropriate safeguards and business constraints for AI outputs.
Collaborating with engineering to develop APIs for LLM applications used by other product components.
Creating automated evaluations to measure the accuracy and performance of LLM-powered systems.
Maintaining and improving existing NLP and AI diagnosis production components.
Developing analytics to monitor system performance and prioritize improvements.
Deploying AI services end-to-end in cloud-native environments using AWS and Kubernetes.
Staying on the cutting edge by researching and testing new AI tools, APIs, and architectures.
Contributing to our conversational AI strategy and helping shape the future of healthcare AI.