Engineering Manager

Tali AI•Toronto, ON

31d•CA$180,000 - CA$230,000•Hybrid

About The Position

Tali AI is a rapidly growing startup in Canada focused on making healthcare more accessible through AI. They are developing a comprehensive clinical operating system that includes an ambient AI scribe, billing agent, scheduling, and clinical decision support. This platform aims to allow clinicians to focus more on their patients and less on administrative tasks. Tali AI's product is already used by thousands of clinicians across various specialties in Canada and the US, integrated with numerous health record systems. The company has achieved significant traction, with multiple commercial product lines, over 15 million patient visits documented, and over 70 years of time saved for clinicians in under three years. They operate in a fast-paced market where speed is crucial and maintaining clinician trust is paramount.

Requirements

7+ years of experience in engineering
2+ years as a manager
5+ years as an IC engineer
Managed people who visibly grew, leveled up, got promoted, or went on to lead.
Experience building or reshaping how a team works (rituals, tooling, culture, process) to solve real business problems.
Player-coach experience at a startup or scale-up.
Scaled a small, high-output team while staying close to the work.
Technical depth across the stack.
Ability to drop into technical work when it's the best use of time.
Reasoning from first principles.
Judicious use of best practices, frameworks, or playbooks, with the ability to set them aside when not a fit.
Strong judgment.
Ability to build the judgment of others.
Ability to take hard feedback, sit with it, and change your mind if necessary.
Ability to deliver hard feedback while remaining radically caring about the other person.

Nice To Haves

Experience in a regulated industry (healthcare, finance, or similar).

Responsibilities

Own growth, performance, 1:1s, and the talent bar for a team of engineers (software, ML, and infrastructure).
Own outcomes end to end, working collaboratively with PM, design, CS, and sales.
Unblock people by addressing small decisions on product, technology, and individual growth.
Coach individuals by debugging root causes to foster self-improving paths.
Set clarity and context by simplifying complex situations for the team.
Model the system by looking for systemic causes of underperformance rather than just symptoms.
Sustain urgency without causing burnout by calibrating energy levels.
Lead an AI-native team, contributing to reinventing industry work practices.
Decompose ambiguous goals into AI-executable work, evaluate outputs, and identify leverage points for AI infrastructure investments.
Raise the bar and build a repeatable management approach, innovating on the craft rather than just running ceremonies.