Staff Software Engineer, AI

About the role

Build better healthcare together

Maple’s purpose is to meet the world's healthcare needs. We’re Canada's leading on-demand healthcare platform, connecting patients with Canadian-licensed doctors, nurse practitioners and specialists. Today, we’re proud that over 8 million patients and 7,000 businesses and government partners have access to same-day, proactive and ongoing care. We enable people to take control of their health and push the boundaries of what’s possible every day.

Bold minds. Big impact. A career at Maple is about making an impact. We empower bold thinking, surround you with an inspiring team and are driven by a motivating purpose. Here, you’ll do work that changes lives—including your own.

The role We’re looking for a Staff Software Engineer, AI to own and ship the next generation of AI-powered healthcare experiences at Maple.

You’ll work inside Product Engineering, partnering with Product, Design, Platform, Security, Privacy, and clinical experts to take ambiguous patient and provider problems from discovery to production. You’ll spend meaningful time designing, writing, reviewing, and operating production code for customer-facing AI capabilities. You’ll also help the wider engineering organization adopt the patterns that make those capabilities reliable, safe, observable, and cost-effective.

This is a hands-on, senior individual contributor role for an engineer who has spent the last few years actively building AI into real products—not just experimenting with models or defining high-level standards. You’ll have direct ownership of a high-impact product area while serving as a technical leader to influence how AI is built and scaled across Maple.

You responsibilities As a Staff AI Engineer, you will:

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Own a meaningful AI product area from problem discovery and technical design through implementation, launch, monitoring, and iteration

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Partner with Product and Design to understand user needs, identify where AI creates real value, and turn ambiguous problems into simple, useful experiences

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Design and build production AI systems using LLMs, agents, retrieval, tool calling, workflow orchestration, and human-in-the-loop patterns where appropriate

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Write production code, review designs and pull requests, debug difficult issues, and participate in operational ownership after launch

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Establish practical evaluation and monitoring approaches that measure product quality, safety, reliability, latency, and cost

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Work with Platform Engineering to make the right decisions about shared services, model providers, infrastructure, and ownership boundaries

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Help teams make sound trade-offs across user experience, clinical quality, privacy, security, reliability, and AI unit economics

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Turn successful product implementations into reusable patterns that help other squads move faster and more safely