Software Engineer, Applied AI/Product (Toronto)

We're a small team obsessed with deep thinking, speed, craftsmanship, and a customer-focused approach, committed to building the best care experience in healthcare with a 30-year horizon.

Our team has built and launched numerous products from 0-to-1, scaled startups from 10 to 300+ employees and from inception to over $1B in valuation, $60M+ ARR, and 100M+ MAU.

We are a group of technologists, physicians, engineers, and designers that are uniquely positioned to design and distribute the product in the healthcare industry.

Traction/PMF

• We have strong product-market fit ($10MM+ ARR in under 14 months).

• We are profitable and funded by top-tier VCs in the healthcare vertical.

• We are looking for a generalist to work together to scale to hundreds of customers. You'll fit in if you want to take ownership of a product, codebase, or company from 1 to 100.

The Role

We’re building an agentic platform at the intersection of medicine and law, transforming complex, high-stakes casework into structured, actionable context. Our system handles patient records, legal workflows, and sensitive decisions, so reliability, clarity, and thoughtful engineering matter deeply.

As a software engineer, you'll work across the full stack: agent architectures, evals, the data and context that feed them, and the surfaces to interact with the platform and its harness.

In a given week, that might mean

• building and shipping a new workflow that takes over a step of the case process, driven by something a physician or our ops team flagged

• designing the eval framework that decides whether an agent's output is reliable enough to ship, while piping the feedback into our synthesized knowledge base

• improving how the platform ingests, indexes, and assembles the right case context for an agent to work from

• adding new tooling to the interfaces users interact with day to day, bringing more of the platform's context and agent capabilities into their workflow

• iterating quickly on a feature with direct input from users, refining the agents based on real-world usage and feedback

• setting the patterns and tooling for AI-native development that the rest of the team builds on

You’ll be a great fit if you

• engineer for correctness and reliability, not just functionality

• are excited to work with frontier models, and care as much about the evals and guardrails around them as the models themselves.

• ship high-quality code fast, and can take a vague problem to a shipped solution without a spec.

• own problems end-to-end, from the agent logic to the interface a user actually uses.

• care about craft and the gap between something that works and something genuinely good to use.

• want to play long-term games with long-term people. We're building for a 30-year horizon, not a quick exit.