Staff Engineer

Tali AI is one of the fastest growing startups in Canada with the mission to use AI and make healthcare more accessible. We are building the AI platform for clinical workflows: automating data collection, processing, and sharing in healthcare, so clinicians can focus on actual care. Our platform is used by thousands of clinicians in Canada and the US, operating across dozens of specialties and deeply integrated with the fragmented landscape of North American health records systems. The technical north star is an operating system for clinical workflows: a platform that healthcare teams configure to automate their data work.

The hard problems are genuinely hard:

• Real-time audio pipelines where latency is felt and accuracy has clinical stakes
• Integrations with a fragmented ecosystem of clinical software: health records, billing, scheduling, referral management, and more
• AI workflows and agents that have to operate reliably in a domain where errors have consequences
• A privacy and compliance posture that has to be right by design

We're moving fast in a market that rewards speed, and operating in a domain where breaking clinician trust is not recoverable.

The role

We're hiring a Staff Engineer who reports to the VP Engineering and works on our highest-stakes, most ambiguous technical problems. This is an execution role. You lead through your output, your judgment, and the quality of the standards you set.

You'll work on projects that are strategically critical and don't yet have a clear shape. You give them shape. You de-risk them, make the irreversible decisions visible, and drive them to completion. Our engineering spans:

• Real-time AI workflows
• Reliability engineering
• AI infrastructure and platform engineering
• GTM engineering
• Security, privacy, and compliance

And you operate across whichever of these a given problem touches. You'll work with a small and growing team that ships daily, reasons clearly, and cares about the clinicians and patients on the other side of every decision.

AI is central to how you work, not as a productivity tool but as a lever for judgment and scope. You use it to take on problems that would otherwise require a team, and you build the architectural foundations (specs, conventions, standards) that let others on the team do the same. When you leave a problem, it's in better shape technically and the team is better equipped to handle the next one.

What we're looking for

• Proven Technical Leadership: You've shipped complex systems from ambiguous starting points. You've made high-stakes architectural decisions under uncertainty and can articulate the tradeoffs clearly enough that others learn from them.
• AI Proficiency: You use AI to decompose large problems, maintain coherence across that execution, and apply genuine architectural judgment to what comes out. You know when the output belongs in the codebase and when it doesn't.
• Product Judgment: You have strong product instincts and use them. On problems that need it, you can define scope, make prioritization calls, and connect technical choices to outcomes without waiting for a PM. You know when "good enough now" beats "perfect later". You also know when it doesn't.
• Force Multiplier: You make people around you better through the quality of your decisions, the visibility of your reasoning, and the standards you set in code review and architecture discussions.

What success looks like

• 3 months: You've taken ownership of a critical project and are driving it to completion with minimal oversight. Your reasoning on the key decisions is legible to the team.
• 6 months: You're a go-to person for some of our hardest problems. You've identified and fixed two or three recurring org-wide bottlenecks. Engineers who've worked with you are making better architectural decisions on their own.
• 1 year: Projects you've led are the reference point for how we execute well. You're shaping technical strategy and the VP Engineering trusts you to run point on anything.