Senior Software Engineer, Health Data

Molecular You is a health tech company that turns complex biomarker science into actionable insight for everyday people. We're looking for a senior engineer to own platform quality — consistency across services, thoughtful tech adoption, security and compliance, and shipping things through to done — in a shop where AI-assisted tooling is part of how we build, not a separate specialty bolted on.

You'll also work closely with our scientific product manager to co-design new health report products, which means you'll need genuine curiosity about how biology works. You don't need a science degree — but if you've ever looked at your own lab results and wondered what they actually mean, that's the instinct.

This isn't a role for an AI specialist. It's a role for a strong engineer who uses AI tools deliberately to move faster, has real judgment about where experimentation ends and production discipline begins, and can explain — clearly, without jargon — why they made the call they made.

 
What you'll own

• Platform quality and consistency — you notice when two services handle errors differently and fix it, not file a ticket
• System architecture and planning — you can take an idea from a rough plan through to production and explain the tradeoffs you made along the way
• Thoughtful introduction of new technology, AI-assisted tooling included — you're skeptical until it earns its place, then you drive adoption through
• Security and compliance balance, including where AI tools touch sensitive data — you've made design decisions where delivery and security were in tension, and you navigated them with judgment, not just caution
• Production improvements from diagnosis through to resolution — you close the loop
• New health report products developed alongside our scientific PM — from clinical question to shipped feature

What we're looking for

Senior production experience with a mature OOP web framework (we run Rails; Django, Spring, or similar counts) and a concurrent, message-driven backend (we run Elixir/Phoenix + RabbitMQ; Go, Kotlin coroutines, or similar counts) — the concepts transfer, and we expect you to ramp onto our specific stack fast

Real depth in at least one of: distributed systems failure modes, database/schema design at scale, or message-queue architecture — depth we mean literally: something you can go several levels past the textbook answer on, unprompted, because you've actually hit the edge of it in production

Practical, hands-on use of AI tooling in real engineering work — not a course or certificate, a specific tool or workflow you built or use daily, plus a clear line you've drawn on what you won't let it touch
At least one example of reducing platform entropy — a migration, deprecation, or refactor that made a system class simpler, not just larger

Experience owning a security or compliance decision end-to-end (HIPAA/PIPEDA context is a plus; the judgment is the requirement)

Clear, direct communication, written and spoken — you can explain a technical trade-off to a scientific PM without either oversimplifying or retreating into jargon

If you meet most of these but not all, we'd still like to hear from you. We're looking for the right person, not the perfect checklist.

 
How we work

 
We use Shape Up: six-week cycles with real autonomy, followed by cooldown periods where you help shape what comes next. You'll have direct access to customer feedback and clinical context. Engineering, product, and science work closely together here — decisions get made in conversations, not committees.
We're a small team. Your voice will carry. So will your follow-through.

 
Our stack

 
Ruby on Rails · Elixir/Phoenix · RabbitMQ · PostgreSQL · Terraform · AWS · GitHub — plus whatever AI-assisted tooling actually earns its place; we're not precious about the vendor