Software Engineer, Gen AI Platform

About Abridge

Abridge was founded in 2018 with the mission of powering deeper understanding in healthcare. Our AI-powered platform was purpose-built for medical conversations, improving clinical documentation efficiencies while enabling clinicians to focus on what matters most—their patients.

Our enterprise-grade technology transforms patient-clinician conversations into structured clinical notes in real-time, with deep EMR integrations. Powered by Linked Evidence and our purpose-built, auditable AI, we are the only company that maps AI-generated summaries to ground truth, helping providers quickly trust and verify the output. As pioneers in generative AI for healthcare, we are setting the industry standards for the responsible deployment of AI across health systems.

We are a growing team of practicing MDs, AI scientists, PhDs, creatives, technologists, and engineers working together to empower people and make care make more sense. We have offices located in the Mission District in San Francisco, the SoHo neighborhood of New York, and East Liberty in Pittsburgh.

The Role

Our generative AI-powered products are making a huge impact in the Healthcare industry. As an AI Platform Engineer, you will collaborate closely with a cross-functional team of researchers, clinical scientists, and product engineers. You will design and build the runtime, orchestration engine, and evaluation platform for agentic orchestration and LLM-driven workflows.

What You’ll Do

• Design and build GenAI systems that turn LLMs into composable, dependable tools—leveraging retrieval, tool use, agentic reasoning, and structured outputs.
• Design and implement a highly reliable and scalable agent runtime: orchestration, shared state and memory, tool-calling interfaces, and scheduling for cost, latency, and quality.
• Build secure, sandboxed execution for agent actions and code; optimize cold start, isolation, and observability.
• Ship unified interfaces for multiple model sizes and providers; integrate with open tool ecosystems such as MCP-style connectors for data and actions.
• Develop an evaluation platform for online and offline assessments, A/B tests, safety checks, and regression gates that improve agent reliability over time.
• Partner with Research to deliver new agent capabilities end to end—from prototype to production.

What You’ll Bring

• Experience building agent applications with tool-calling, context engineering, or open connector integrations.
• Fluency with LLM APIs, prompting strategies, and orchestration patterns (e.g., LangChain, LlamaIndex, or custom pipelines).
• Experience with retrieval systems (e.g., semantic and lexical retrieval, vector DBs, efficient kNN), function calling, tool use, or agentic workflows.
• Strong coding skills in one or more of: Python, Java, Go. Comfortable with service design, APIs, and data models for high-throughput systems.
• Working knowledge of containers and kubernetes concepts. Familiarity with metrics, tracing, on-call rotations, and incident response practices.
• Self-motivated with a willingness to take ownership.
• Strong communication skills and ability to work collaboratively in a team environment.

Bonus Points If…

• Prior work on agent orchestration pipelines: task routing, planning, memory graphs, vector search, or browser automation.
• Experience with evaluations, preference optimization, or RL to improve LLM reliability.