Software Engineer, Growth

TL;DR

We’re looking for a full-stack engineer who is willing to get obsessed with HotDoc’s growth engine. One sprint you might be shipping a feature-flagged experiment on the patient booking funnel and hosting the readout post wrap-up; the next, you’re turning what you learned from your last five experiments into an AI workflow the whole Growth function adopts. You’ll own the experimentation loop for one of our mission-based teams end to end: frame the bet with your Growth PM, ship the smallest thing that tests it, instrument it properly, and own the readout. 

Engineering craft + growth instincts + a need to know why = you.

Why HotDoc?

HotDoc’s mission is a better healthcare experience for every Australian. For Growth, that comes down to a single question: did the patient get the help they needed? People often reach us at their most vulnerable, so we run disciplined experiments across the whole patient journey to keep improving their odds of finding the right care. Your job within the team is to drive our patient growth engine: turning the first moment a patient hears about HotDoc into a lifelong habit of managing their healthcare with us.

Every experiment you ship reaches the 2 million+ patients who use HotDoc each month. And your impact won’t stop there: working closely with the Head of Growth Engineering, you’ll shape the AI-first workflows and tooling the whole Growth function runs on, and help define what great Growth Engineering looks like at HotDoc. You’re joining early enough that this is genuinely yours to build. You’re creating the function, not inheriting it.

What we’re looking for

• 5+ years building and growing B2C products in high-performing teams, ideally with some growth work on marketplace or community platforms.
• Strong product sense and UX taste, especially for products where more than one kind of user has to win at once. For us that’s a three-sided marketplace: clinics, patients and practitioners.
• Full-stack as a preference. You have strong fundamentals across the stack, and the judgment to know when AI can carry you across a gap and when the better move is tapping a teammate who has the expertise.
• AI-first in how you work. You have used Claude, Codex or equivalents in previous roles and can speak to the specific leverage they gave you.
• Clear, proactive communication. You surface technical constraints, measurement risks and scope issues early rather than discovering them post launch.
• Comfortable rolling back your own work when the data says so. A good experiment always wins by giving you a clear answer, even when the answer is that the variant should not ship.

Bonus points for any of:

• Production experience with A/B testing, feature flags and clickstream / analytics instrumentation, i.e. translation an experiment brief into primary/secondary/guardrail metric, cohorting users appropriately, and selecting appropriate activation metrics for our reporting.
• An understanding of growth accounting: north star metrics and the drivers that move them.
• Hands-on time with GrowthBook, Amplitude or similar analytics tooling.
• Healthcare or another regulated consumer domain where trust and respecting sensitive user data are first-class concerns.

What you’ll do

You’ll be embedded in a mission-based team, working daily with your Growth PM, designer and data peers.

• Choose experiments deliberately. Before building anything, agree with your Growth PM which metric the experiment is meant to move and why it’s the most valuable thing to test right now.
• Run each experiment end to end. Coauthor a hypothesis with your Growth PM, build the smallest version that can test it, set up the tracking, and analyse the results yourself before presenting them back to the team.
• Ship safely. Every experiment sits behind a feature flag so it can be switched off instantly, guardrail metrics are agreed before launch, and opt-out, consent and patient safety are requirements from day one.
• Follow through once the results are in. Build winning experiments into the product properly, and fully remove losing ones, including their flags and tracking, so the codebase stays clean.
• Write down what you learn. Document the outcome of every experiment, win or lose, somewhere the whole Growth function can find and reuse it.
• Make your best workflows reusable. When you find a better way of working, especially with AI tools, turn it into a skill or workflow the rest of the function can adopt.
• Be a good citizen of the codebase. Move at speed through tight sprints, but hold the team’s engineering standards so the product engineers you build alongside trust what you ship.

What success looks like

By 3 months: you understand your team’s growth model, the drivers that move its metrics, and where the opportunities are. Your first experiments are well and truly live, and you’re forming strong opinions about how next quarter’s roadmap should unfold.

By 6 months: you’re running a steady, high-quality cadence of experiments against the highest-leverage drivers, and you can speak confidently to how your work has moved the metrics your team owns. You’ve turned your wins and misses into reusable AI workflows the wider Growth function has started to adopt.

By 12 months: you’ve made a visible dent in your mission’s growth story. Not just shipped experiments, but moved the numbers that matter to your team. Along the way you’ve uplifted the experimentation and measurement tooling so others across HotDoc can share in those wins, and you’re actively shaping how engineering and experimentation get done here.

Our tech stack

• Backend: Ruby on Rails (modular monolith), PostgreSQL, Redis, Sidekiq
• Frontend: Ember.js
• Experimentation and data: GrowthBook, Metabase, BigQuery, dbt
• AI tooling: Claude Code, with agent-driven workflows across the team