Make the agents that run healthcare's front desk smarter, faster, and more reliable. Own the models, evaluation, and training loops behind millions of real conversations, and turn what happens on the phone every day into a system that keeps getting better.

What you'll do

You'll own the machine learning behind Dodo's agents, from the models in the call path to the evaluation that tells us whether they work.

Improve how our agents understand callers, choose actions, and complete bookings across voice, text, and web

Build evaluation pipelines and judges that measure agent quality on real conversations, catch regressions, and gate every model and prompt change

Fine-tune, distill, and route models to hit the latency and cost budgets of real-time voice while improving quality

Build the data loops that turn conversation outcomes, practice feedback, and corrections into training signal

Own speech and voice quality where it meets the model: transcription, turn-taking, interruption handling, and latency

Work with product engineers to ship model improvements into production and measure their effect on bookings and customer outcomes

Set the direction for how Dodo uses models as the space changes month to month

Who you are

You've shipped ML into production systems people depend on, and you care about measured results more than demos.

3+ years building and deploying machine learning or LLM systems in production

Deep experience with LLM evaluation, fine-tuning, prompt and model routing, or speech systems

Strong engineering fundamentals: you write production code and own what you ship

Rigorous about measurement, and comfortable designing evaluations when none exist

Pragmatic about latency, cost, and reliability in real-time systems

Comfortable working with messy real-world conversation data and the privacy obligations of healthcare

Excited to work in person with a small team of exceptional engineers in San Francisco