Early-career Software Engineer building and iterating on agentic LLM systems, RAG pipelines, evaluation frameworks, and backend/frontend components for AI-powered clinical documentation at Abridge. Requires CS degree or equivalent plus hands-on GenAI experience from projects, internships or coursework.
Build and productionize agentic LLM workflows for clinical documentation at Abridge, owning architecture, rigorous evaluation frameworks, and deployment of reliable GenAI systems in healthcare. Requires 3+ years building production systems with 1-2 years on LLMs or agentic products, strong Python skills, and hybrid work in SF or NYC.
Combines clinical expertise (MD/PA/DO) with AI to develop and refine tools for clinical documentation, decision support, and risk adjustment. Defines quality standards, optimizes prompts, evaluates outputs, and collaborates with engineering teams to enhance clinician efficiency.
Builds and optimizes scalable ML inference infrastructure using Kubernetes and GPU resources to deploy production AI models with low latency. Collaborates with ML research and product teams on model serving, orchestration, and compute efficiency.
Early-career Software Engineer building and iterating on agentic LLM systems, RAG pipelines, evaluation frameworks, and backend/frontend components for AI-powered clinical documentation at Abridge. Requires CS degree or equivalent plus hands-on GenAI experience from projects, internships or coursework.
Build and productionize agentic LLM workflows for clinical documentation at Abridge, owning architecture, rigorous evaluation frameworks, and deployment of reliable GenAI systems in healthcare. Requires 3+ years building production systems with 1-2 years on LLMs or agentic products, strong Python skills, and hybrid work in SF or NYC.
Combines clinical expertise (MD/PA/DO) with AI to develop and refine tools for clinical documentation, decision support, and risk adjustment. Defines quality standards, optimizes prompts, evaluates outputs, and collaborates with engineering teams to enhance clinician efficiency.
Builds and optimizes scalable ML inference infrastructure using Kubernetes and GPU resources to deploy production AI models with low latency. Collaborates with ML research and product teams on model serving, orchestration, and compute efficiency.