Quantitative Developer, Investment Platforms Location:

Montreal (Hybrid: 3 days in-office, 2 days remote) Level:

Senior Employment Type:

Full time

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About Nymbus Capital Nymbus Capital is a Montreal-based

systematic investment firm specializing in fixed income and managed futures

solutions. We stand out as the only systematic and AI-centric bond manager in Canada. Our investment process is built and run by

one integrated team of quantitative researchers, strategists and developers : asset allocation and security selection are driven by systems we build together, not by individual discretionary books.

We've become known as “the nerds of the street” and proudly wear that tag . Since our founding, we've grown to manage

over $1.9 billion in assets, an achievement driven by consistent top-percentile performance . You will join a team where developers, researchers and strategists build the investment process together.

Role Overview As our

Quantitative Developer, Investment Platforms , you will own the systems that run our investment process end to end: the data platform that feeds every model and report, the research and backtesting stack, portfolio construction and trade execution, and the internal applications built on top. You will work as a peer to our quantitative researchers and strategists, not a ticket-taker, with a direct say in how models are designed, validated and deployed.

This is a

hands-on role . You will personally write code, build integrations, deploy applications and resolve production issues. You will take ownership, learn unfamiliar systems quickly and move work forward independently.



You will turn business needs into working software and help the team deliver.

We build

AI-native : you will use AI coding agents in depth and help define how the whole team builds with them.

What will your typical day look like?

Own the platform architecture : Set technical priorities, make architectural decisions, and drive the evolution of our data, research and trading infrastructure with high autonomy.

Ship production systems : Take projects from conception to deployment, from refactoring research code to building new capabilities.

Co-build models with researchers : Work alongside quantitative researchers and strategists from hypothesis to deployment, turning complex models, signals and data requirements into robust, tested and monitored production code.

Lead AI-native development : Evolve how the team works with AI coding agents (repository context, guardrails, automated testing and review workflows) to ship faster without lowering the bar.

Put AI agents to work : Proactively identify useful applications for AI agents across investment, research and operations. Build and test them with the team, and verify their results before production use.

Enable the business : Build high-quality dashboards and internal tools that let the investment, operations and client teams interact with trading, risk and research systems.

Own financial correctness : Reconcile positions, cash,



transactions, prices, NAV and performance across custodians, fund administrators and internal systems. Detect missing or stale data, investigate differences and make failures visible before downstream use.

Qualifications

Senior Development Background:

5+ years of professional experience building and maintaining production-grade systems, ideally data-intensive or numerical applications.

Backend:

Strong Python and SQL/PostgreSQL skills, including data modeling, API design, query performance and safe database changes. Experience with FastAPI, SQLAlchemy/SQLModel and Alembic, or comparable tools, is relevant.

AI-Assisted Development:

Demonstrated ability to use AI coding agents to deliver production software. You can provide repository context, break work into clear tasks, review generated code, test financial logic and verify deployed behavior. You remain accountable for the result.

AI Agent Development:

Practical knowledge of building AI agents that use tools, APIs and data to complete tasks. You understand how to provide context, connect tools, evaluate results and make agent workflows reliable.

Quantitative Fluency:

Comfort with statistics, linear algebra and numerical computing (NumPy, pandas or Polars), enough to read a research memo and challenge how a model is implemented.

Machine Learning and Deep Learning:

Working knowledge of machine learning and deep learning, including neural networks, model training, validation and evaluation. You can understand researchers' approaches, ask informed questions about data and model choices, and assess whether results hold up on new data. You

📌 Quantitative Developer (Winnipeg)
🏢 Nymbus Capital
📍 Winnipeg