Abridge
Software Engineer- Early Careers
About this role
Abridge is hiring early-career Software Engineers to build AI-powered clinical systems that transform patient-clinician conversations into structured medical notes. You'll develop agentic LLM systems, evaluation frameworks, and production features while collaborating with researchers, clinicians, and engineers in a fast-paced healthcare AI environment.
What you'll do
- Build and iterate on agentic LLM systems including retrieval pipelines, tool use, and workflow chaining
- Develop evaluation frameworks to measure accuracy, robustness, and clinical reliability of AI systems
- Contribute to backend and frontend systems across the Abridge platform
- Prototype with new models, prompting techniques, and orchestration tools like LangChain and LlamaIndex
- Build monitoring and observability systems for LLM workflows in production
- Collaborate across ML, infrastructure, product, and clinical teams to understand user needs
What they're looking for
- Generative AI and LLM integration experience
- Agentic AI workflows and prompt chaining
- Retrieval-Augmented Generation (RAG) pipelines
- LLM orchestration frameworks (LangChain, LlamaIndex)
- Backend and frontend development
- Model evaluation and testing methodologies
- Production monitoring and observability
- AI tooling proficiency (using AI for coding and debugging)
Benefits
- High autonomy and responsibility for an early-career role
- Accelerated learning and career growth opportunities
- Path to future leadership positions
- Work on healthcare AI with real-world clinical impact
- Collaborative team environment with experienced mentors
- 5-day onsite work in SF or NYC office
Opens the official application on the employer’s site. No login required.
Abridge
Abridge builds an AI platform that transforms healthcare conversations into clinical documentation using advanced natural language processing and generative AI. The company is hiring ML infrastructure engineers, implementation engineers, and machine learning scientists to develop model serving systems, manage customer deployments, and advance medical NLP research.
- Website
- abridge.com
Likely interview questions
- Can you walk us through a generative AI project you've built—what was your approach to handling model limitations or failures?
- Describe your experience with LLM orchestration concepts like tool use or retrieval pipelines. How did you evaluate whether your implementation was working?