Software Engineer, AI Research & Prototyping

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

Voice 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. The hard part is knowing which ones, proving it with evidence, and getting that knowledge into the hands of the team.

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.

This is not a research-for-publication role, and it is not a production ownership role. It is for someone who prototypes fast, measures honestly, and makes the people around them smarter.

What You'll Do

• Track emerging techniques in voice AI, LLM reasoning, and agent systems, and identify which ones matter for us

• Design structured experiments with real controls: know when a result is signal and when it is noise

• Build rapid prototypes and test them against real conversation data

• 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

• Build the internal knowledge base for how and why our AI stack works the way it does

What We're Looking For

Required

• 3+ years of software engineering or applied ML experience

• Strong Python 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, not from Twitter

• Ideas are adopted or killed based on evidence, quickly, instead of lingering as opinions

• 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

Why This Role Matters

Every voice AI company is betting on which techniques to adopt and when. Most make those bets on intuition and vendor claims. The systems handling patient conversations deserve better: decisions backed by evidence from our own data. This role is how Sage stays at the frontier without gambling on it.