Backend Software Engineer - New Bets

Heidi · Londonmid

In short

▸Construyes productos nuevos de cero a producción en equipo pequeño, con impacto inmediato en clínicos reales.
▸Tienes total ownership: desde el front end (React/TS) hasta servicios en Python y sistemas de IA (prompting, recuperación, evaluación).
▸La mayoría de los proyectos mueren, pero los que sobreviven heredan tu código — una oportunidad de impacto rápido y claro.

Proficiency in English is required for collaboration with global teams.

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What they ask for

✓3+ años de software en producción, priorizando resultados sobre tiempo en puesto
✓Fluidez full-stack: Python (back) y React/TypeScript (front)
✓Experiencia práctica con sistemas de LLM: prompting, recuperación, embeddings y evaluación
✓Capacidad de tomar decisiones sobre lo suficientemente bueno para un prototipo vs. lo que debe ser sólido
✓Exposición al trabajo con clínicos y comprensión del workflow clínico real
✓Habilidad para construir pipelines de instrumentación (uso, latencia, fallos) para tomar decisiones basadas en datos

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PythonReactTypeScriptLLMPromptingRetrievalEmbeddingsEvaluationVoice pipelinesProduction software

Who should you write to at Heidi?

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Who is Heidi? Heidi is building an AI Care Partner that supports clinicians every step of the way, from documentation to delivery of care. We exist to double healthcare’s capacity while keeping care deeply human. In the last 18 months, Heidi has returned more than 18 million hours to clinicians and supported over 73 million patient visits. Today, more than two million patient visits each week are powered by Heidi across 116 countries and over 110 languages. Founded by clinicians, Heidi brings together clinicians, engineers, designers, scientists, creatives, and mathematicians, working with a shared purpose: to strengthen the human connection at the heart of healthcare. Backed by nearly $100 million in total funding, Heidi is expanding across the USA, UK, Canada, and Europe, partnering with major health systems including the NHS, Beth Israel Lahey Health, MaineGeneral, and Monash Health, among others. We move quickly where it matters and stay grounded in what’s proven, shaping healthcare’s next era. Ready for the challenge? The Role The New Bets squad builds Heidi's next products. Scribe already powers millions of patient visits a week, and Evidence and Remote are scaling behind it. Your job, alongside the New Bets PM and Team, is to build what comes next. This is zero-to-one engineering with a distribution advantage most early products never get. Your v1s don't launch into silence, instead they go in front of clinicians who already open Heidi every day, and who will tell you within a week whether you have built something they need. The squad is small on purpose. You'll build the whole thing: the React front end, the Python services behind it, and the prompts, retrieval and eval harnesses that make an LLM feature safe enough to put in front of a clinician. When a bet needs an onboarding flow, a data migration or a quick internal dashboard, that's you too. There's no one else to hand it to, so you will own the build from PoC through to production. Some of what you build will be killed. Ultimately, most bets don't survive contact with a real clinic, and the winners graduate into staffed product lines that inherit your code. For this to work, the code that dies has to have been fast to write, and the code that lives has to be worth inheriting. To fit in well with this team, you need to be the engineer who ships, ideally with: a few years of production software behind you, real judgment about what's good enough for a prototype versus what has to be right, and the appetite to own more surface area than any normal role would give you. If you're mid-level and impatient about it, this role is a fast-track to exposing yourself to complex problems and pushing towards a Senior level. What you'll do: Build new products end to end: React/TypeScript front ends, Python services, and the LLM and voice pipelines in between Turn a one-page thesis into a working v0 in days, then decide with the PM what deserves a v1 Make AI features trustworthy and compliant: prompting, retrieval and evals that hold up in a real clinic, not just in a demo Ship weekly, demo what you shipped, and let real usage settle arguments Spend time with clinicians to deeply understand how they actually work, and the blockers they face daily Work with our commercial teams to understand the business case behind what we build Instrument everything: usage, latency, failure modes.

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