AI Software Engineer

Location:

Canada or United States (US time zones)

Experience:

Typically 2+ years of relevant applied work, although we care substantially more about the quality and difficulty of the problems you have solved than the number of years you have been solving them.

We are looking for exceptional quantitative thinkers to build agentic systems that help people make better decisions. This is not primarily a conventional data science role, an LLM application-development role, or a prompt-engineering role. We are interested in people who want to understand how intelligent systems can reason over complex problems, use tools and models effectively, learn from evidence, and become systematically better over time.

A central question in this role is:
How do we make an agentic system measurably better at the decisions it is intended to support? That means moving beyond intuition, demos and prompt alchemy. You will think about objectives, observability, feedback, evaluation datasets, statistical uncertainty, experiments, simulation, failure modes and improvement loops. You should be interested not only in building agents, but in developing rigorous ways for those agents—and the systems around them—to improve.

You will work on ambiguous, consequential enterprise problems where the answer is rarely “use an LLM.” Sometimes the right solution will involve an agent. Sometimes it will involve statistical modelling, optimization, machine learning, simulation, rules, or a surprisingly simple piece of mathematics. Frequently it will involve several of these working together. We want people who are technically ambitious, intellectually demanding, empirically minded and impatient with hand‑waving.

Design and build agentic decision‑support systems that can reason over data, interact with tools and models, maintain state, decompose complex problems and support consequential human decisions.

Develop mechanisms for systematic improvement. Design evaluation frameworks, feedback loops, benchmarks, experiments and instrumentation that allow us to determine where a system fails, why it fails and whether a proposed change genuinely improves it.

Turn “make the agent better” into a quantitative problem. Define measurable objectives and failure modes; build representative test sets; analyze performance distributions rather than anecdotes; and distinguish real improvement from movement on a convenient metric.

Explore self‑improving agentic architectures. This may involve reflection and critique, adaptive tool or model selection, memory, search, planning, generated training/evaluation data, policy improvement, human feedback, simulation or other mechanisms. We are interested in what works—not in allegiance to a particular architecture.

Bring statistical and machine‑learning judgment to agentic systems. Use probabilistic modelling, statistical inference, ML, optimization or causal reasoning when they make the system more useful, more reliable or easier to improve.

Generate high‑quality software using AI coding systems. We expect you to work AI‑natively. The objective is not to demonstrate how quickly you can type Python; it is to produce excellent software.

Own difficult problems end to end. Work from an initially ambiguous business or decision problem through problem formulation, system design, implementation, evaluation and deployment. Challenge the problem definition. A technically sophisticated solution to the wrong problem is still the wrong solution.

You should be comfortable questioning assumptions and reframing what is being optimized. Work directly with senior technical and business stakeholders. Explain complex ideas precisely without hiding behind…