Research Software Engineer (hybrid Montreal)

Location: Montréal Employment type: Regular, full-time Team: Technology / Engineering Start date: As soon as possible. We're a fintech firm with more than three decades spent pairing serious engineering with unusually capable people to build and run financial systems in markets across the globe. We prize independence and the ability to change direction quickly when an opportunity opens, so we operate on our own platform, deploy our own capital, and own the risk that comes with it. We work with curiosity, mutual respect, and open minds. The people who do well here believe that how the work gets done matters as much as the work itself. Expect a high bar, a premium on integrity and invention, and a real appetite for questioning the consensus. This role lives where research meets infrastructure: you'll form hypotheses, test them fast with hands-on analysis, and then build the production systems that put the winning ones to work. LLMs are a core part of how we build here: a first-class tool, not a side experiment. The engineers who thrive are the ones who can drive an agentic harness as naturally as they read a profiler, and who know precisely where each of those tools stops being trustworthy. How You Will Make an Impact Discovery & Research Grow our event-driven approach across exchange feeds, market news, social platforms, and infrastructure telemetry. Stand up quick analyses in Python/Jupyter to gauge signal quality, then convert what you learn into concrete system improvements. LLM & Agentic Systems Design and operate agentic harnesses: tooling, context management, evals, guardrails (that do meaningful work against our data and infrastructure, and own their quality once they're live). Deploy LLMs where they truly earn their place (extraction, classification, triage, faster research) and knowingly skip them where they don't. Infrastructure & Technical Leadership Own performance end to end, from the network edge through in-memory stores — instrumenting, monitoring, and debugging live systems shoulder to shoulder with operations while keeping SLOs tight. Drive green-field builds, design reviews, and post-mortems. Requirements What You Bring Core Three to five years building real-time or data-intensive systems (we care more about depth and trajectory than the precise year count). Real depth with LLMs rather than surface familiarity; how these models actually behave (context windows and their failure modes, tool use, structured output, cost and latency trade-offs, keeping hallucination in check) and how to construct the scaffolding around one: tools, memory, retries, evals, and sensible human-in-the-loop limits. Come ready to walk us through something real you shipped, end to end, including what went wrong. Solid engineering fundamentals in Python, Go, or Rust. That's a preference, not a gate: deep systems experience in another serious language carries over. Strong grasp of network programming and protocols: TCP/UDP/IP, DNS, BGP, HTTP(S), WebSocket, QUIC. Taking rapid POCs to production while confirming statistical significance, iterating quickly, shipping, and explaining the outcome clearly to technical and non-technical audiences alike. A Bachelor's or Master's in Computer Science, Data Science, Mathematics, or a comparable field. Who Thrives Here This is a competitive, high-stakes setting, and we're looking for people who are drawn to that rather than merely willing to put up with it. You'll probably recognize yourself here if: You want problems that are truly hard and measurable, where the scoreboard is real and visible inside the team. You move quickly without cutting corners, and you'd rather ship, measure, and adjust than deliberate indefinitely.