Who You'll Join

A digital clinic scaling to become an AI-native organization is building its foundational AI/ML team. You'll join a small group of engineers tasked with enabling the entire product suite to move fast and ship AI-powered solutions. This is an opportunity to shape how a large healthcare platform integrates AI at its core.

What You'll Do

1. Design and deploy end-to-end AI-powered systems using LLMs, RAG, and agentic architectures in production.
2. Build critical AI features including chatbots, recommendation engines, and search functionality for millions of users.
3. Develop ML pipelines and platform infrastructure enabling rapid model experimentation, fine-tuning, and deployment.
4. Collaborate across product, data, and engineering teams to translate healthcare data into intelligent workflows.
5. Drive technical decisions and architecture across the AI/ML stack, setting standards for quality and safety.

Who You Are

1. Write production Python code. Demonstrate mastery of system design and distributed systems complexity.
2. Built and shipped AI features end-to-end. You understand data pipelines, model serving, and inference infrastructure.
3. Have six or more years of software engineering experience, with hands-on work in LLMs or modern AI systems.
4. Think like a product engineer. You care about outcomes, not just implementation details.
5. Comfortable working without AI coding assistants in interviews to demonstrate foundational engineering strength.
6. Bonus: Experience building chatbots, recommendation engines, or working with OpenAI APIs in production systems.

Tech Stack

Python, LLMs, RAG, agents, PyTorch or TensorFlow, system design patterns for AI workloads.

Why You'll Thrive

1. You'll own high-impact problems at a company where a handful of engineers shape the entire product direction.
2. Compensation of $220k–$300k with flexibility for exceptional candidates, plus equity in a growing healthcare AI platform.
3. The team is still foundational, meaning you'll influence architecture and tooling decisions from the ground up.
4. You'll work on real healthcare problems with real users, not research or prototypes. Shipping and outcomes matter.
5. Your team actively learns new AI technologies rather than defending legacy systems, so you won't get stuck.