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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 Required What We're Looking For 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
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.