Staff AI Engineer

Canada·TorontoFull-Time Employeelead

OtherStaff Ai Engineer

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Quick Summary

Key Responsibilities

AI Frameworks and agents: LangChain, LangGraph, CopilotKit (for Generative UI patterns) Models and foundation services: AWS Bedrock, OpenAI / Anthropic APIs Observability,

Technical Tools

OtherStaff Ai Engineer

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.

We're looking for a Staff AI Engineer to help our engineering teams build, evaluate, and scale AI features across the product — and set the bar for what great looks like.

In this role, you'll design the evaluation, safety, and agent execution patterns that every squad builds to, stay hands-on with reference implementations and evaluation harnesses, and coach engineers across the org so squads can ship AI features safely and confidently. You'll work closely with Product Engineering, Platform Engineering, Product and our Security, Privacy, and Clinical partners — turning a fast-moving, regulated healthcare environment into a place where AI can scale with the quality patients and providers deserve.

As a Staff AI Engineer, you will:

Set the guardrails, evaluation strategy, and observability standards that keep AI feature quality, safety, and technical health high across the product

Shape the model and provider strategy, usage patterns, and cost controls that keep AI unit economics healthy as we scale

Define the evaluation strategy, agent execution architecture, and reference patterns that the teams building and maintaining AI features build to

Mentor and coach squads and engineers across the org so they can safely do more of the AI work themselves, against Maple's standards

Partner with Product and Engineering squads on agentic feature development as a senior technical partner and reviewer

Co-define the AI governance model with Security, Privacy, Legal, and Clinical partners — including review criteria, PHI handling expectations, and escalation paths

Translate technical AI choices — cost, clinician efficiency, patient experience, and risk — into clear business outcomes for executive and cross-functional audiences

In your first 90 days, you'll complete a landscape assessment of Maple's current AI features, evaluation coverage, cost profile, and governance gaps. You'll publish an initial AI standards and best-practices guide covering prompt and agent design, evaluation expectations, safety and PHI handling, and cost hygiene — and stand up an initial evaluation strategy and reference harness that at least one product squad has adopted for an in-flight feature. By month three, you'll have agreed on the ownership interface with Platform Engineering and built the working relationships across Product, Security, Privacy, and Clinical you'll rely on going forward.

Over your first 12–18 months, you'll roll out a company-wide agent execution architecture adopted by the majority of squads shipping AI features, stand up production-grade monitoring for quality, safety, cost, and latency with clear SLOs, and deliver measurable AI cost optimization against your v1 baseline. Every customer-facing AI feature will be covered by an approved evaluation harness and monitoring dashboard, and squads will be independently shipping AI features to Maple's standards

8+ years of software engineering experience, with 2+ years in a Staff or Principal-level applied AI or ML engineering role

Demonstrated ownership of agentic or LLM-powered systems in production — including evaluation, guardrails, and cost and latency tuning at scale

A strong track record designing evaluation strategies for non-deterministic systems — offline evals, online evals, human-in-the-loop, regression harnesses

Deep understanding of agent execution patterns (tool use, planning, memory, orchestration frameworks) and their trade-offs

Experience defining AI quality and monitoring practices — quality metrics, hallucination and safety controls, drift detection, incident response

A builder's mindset — a product engineer with deep empathy for user and customer impact, comfortable getting hands-on with code, prototypes, and reference implementations

Proven ability to influence — mentoring senior engineers, setting standards across squads, and partnering with platform teams

Excellent written and verbal communication; you can translate confidently between executives, product, clinical and compliance stakeholders, and deep technical teams

Responsibilities

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Requirements

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We don't currently use artificial intelligence (AI) or automated tools to screen, assess or select candidates. Every application is thoughtfully reviewed by our Talent Acquisition team or hiring managers.