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Join Tech @ Ro to build the future of healthcare, from the ground up!
At Ro, we believe that when people achieve their health goals, they can achieve their life goals. The highest-leverage way to move society forward is to give people their health, and the current healthcare system isn’t built to do that. It was built to bill, not to serve patients.
We’re building a new system. One where the patient is in control. One designed from scratch for the digital age.
At Ro, technology isn’t just a function… It's core to how we deliver care. We’ve built a vertically integrated healthcare platform that connects telehealth, diagnostics, pharmacy, and logistics into a seamless, end-to-end experience for millions of patients.
…and we’re just getting started.
As part of Tech @ Ro, you’ll work on systems that operate at scale, with an opportunity to:
- Solve complex, high-concurrency problems across a full-stack platform
- Build and ship quickly with tight feedback loops and real-world impact
- Own systems end-to-end, from architecture to production performance
- Work alongside experienced operators, technical leaders, and clinicians
- Help define how modern healthcare should be delivered
We’re a performance-driven team with a strong sense of ownership and urgency. We move fast, learn quickly, and hold a high bar for what we build, and do so with a big heart — because patients depend on it.
If you’re motivated by impact, scale, and the chance to help lead the patient revolution, come build with us.
The Role
We're hiring an AI Engineer to join a new team we're building from the ground up alongside a Senior AI Lead and an Engineering Manager. You'll build AI-powered features from prototype through production while learning from experienced AI engineers. Most of your work will involve building orchestration layers, prompts, evaluations, retrieval systems, and tooling that make LLMs reliable enough for real-world healthcare operations.
What You'll Do
- Build and ship production LLM-powered features across patient-facing products, clinical workflows, and internal tools.
- Build the AI application layer, including prompt orchestration, tool calling, retrieval (RAG), agent workflows, and structured outputs.
- Develop and iterate on evaluation suites, including synthetic test datasets, LLM-as-a-judge evaluations, regression tests, and human review workflows to improve quality over time.
- Write the code that connects LLMs to Ro's internal systems, APIs, and operational workflows.
- Partner closely with EHR operators, Patient Advocates, and clinical teams to understand the workflows you're automating and identify opportunities for AI.
- Prototype quickly, ship early, measure outcomes, and iterate based on real-world usage.
- Contribute to AI infrastructure, tooling, and engineering best practices alongside senior engineers.
What You’ll Bring to the Team
- 1–4 years building software, with hands-on experience building LLM-powered applications through work, internships, research, school, hackathons, or side projects.
- Strong Python engineer with experience building backend services or developer tools.
- Comfortable using AI-assisted development tools throughout the software development lifecycle (or excited to learn).
- You've built at least one LLM-powered application or feature that you're excited to walk us through, including the technical decisions you made and what you learned.
- You enjoy learning quickly, experimenting with new technologies, and working in an environment where best practices are still evolving.
- You have strong problem-solving skills and are comfortable working through ambiguity with support from experienced teammates.
- Bonus: Experience with orchestration frameworks (e.g. LangChain, LangGraph, AgentCore), RAG architectures, vector databases, prompt/version management, or evaluation and observability platforms (e.g. LangSmith, Braintrust, Arize Phoenix).
A note on reporting structure
This is a new function at Ro, and we're being deliberate about not over-defining it. Your manager and where you sit on the org chart will depend on the specific shape of the team we end up with. We'd rather find the right people and figure out the lines around them than pre-draw boxes and miss great candidates. If that ambiguity is a deal-breaker, this isn't the right role; if it sounds like an opportunity, we want to talk.
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