About Sage Care
Sage Care is a fast-growing, early-stage healthcare startup founded by exceptional leaders from Apple, Uber, Carbon Health and backed by top-tier venture capital (General Catalyst, Chelsea Clinton). With a strong customer pipeline, Sage Care is transforming healthcare by simplifying care navigation.
Our platform makes it easier for patients to find the right doctor, helps providers focus on those who need them most, and ensures faster access to care, delivering better care and stronger economic outcomes at scale through harnessing the latest AI innovations.
Building on our successful collaborations with health systems across the U.S., we have expanded internationally to the MENA region. We are now partnering with health systems there to deploy our AI-powered care navigation platform.
About the Role
AI is moving fast. New models, new orchestration patterns, and new evaluation techniques appear every month, and some of them would make our agents meaningfully better.
We are hiring a Software Engineer to own that pipeline from idea to evidence.
You will stay close to what is emerging in the research community and the voice AI ecosystem, design experiments we can trust, and turn promising ideas into tested prototypes on real production data. Just as importantly, you will teach: every investigation you run ends in something the team can use, whether that is a benchmarked prototype, a technical deep-dive, or a clear recommendation with evidence behind it.
What You'll Do
Track emerging techniques in voice AI and agents orchestration, and identify which ones matter for us
Build rapid prototypes and test them against real conversation data or within engineering development process
Run head-to-head evaluations of models, providers, and techniques (reasoning approaches, speech models, orchestration patterns)
Turn every investigation into a team-usable artifact: a benchmark, a written deep-dive, a tech talk, or a recommendation with evidence
Work with platform engineers to hand off validated ideas for production implementation
What We're Looking For
Required
3+ years of software engineering or applied ML experience
Strong coding skills; able to build and run your own experiments end-to-end without infrastructure support
Hands-on experience with LLMs: prompting, evaluation, and an intuition for how model behavior changes across techniques and providers
Experimental rigor: experience designing tests with controls, baselines, and honest measurement
A track record of teaching or knowledge transfer in some form: teaching or TA experience, workshops, technical writing, internal tech talks, well-documented open source, or developer education
Intellectual honesty: comfortable reporting that a promising idea did not work
Nice to Have
Advanced degree (MS/PhD) in CS, ML, or a related field, or equivalent research experience
Experience with voice or speech systems (STT, TTS, real-time pipelines)
Publications, technical blog posts, or open-source work we can read
Experience taking a prototype through to production with an engineering team
Experience evaluating AI systems in healthcare or other high-stakes domains
What Success Looks Like
The team learns about relevant new techniques from you
Ideas are adopted or killed based on evidence
Validated prototypes hand off cleanly to platform engineers, with the reasoning documented
Six months in, engineers across the team can explain why our stack makes the choices it makes
Our AI decisions get faster and more confident because the evidence base keeps growing