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Quantitative Developer

Job in Montreal, Montréal, Province de Québec, G4F, Canada
Listing for: Nymbus Capital Inc.
Full Time position
Listed on 2026-10-18
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Python, Backend Developer, Software Engineer
Salary/Wage Range or Industry Benchmark: 150000 - 190000 CAD Yearly CAD 150000.00 190000.00 YEAR
Job Description & How to Apply Below
Location: Montreal

Quantitative Developer, Investment Platforms

Location:

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

Employment Type:

Full-time
Industry:  Financial Services
Functions:  Investment Management, Software Development

La version française suit ci-dessous.

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.

We build  AI-native . You will use AI coding agents in depth throughout your workflow, from system design and implementation to testing, review and documentation, and you will help define how the whole team builds with them: the context, guardrails, evaluation and review practices that keep agent-assisted code production-grade.

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, moving fluidly between refactoring legacy research code and building new capabilities from scratch.

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.

Build the technical foundation :
Define development standards, testing and validation frameworks, and data contracts; mentor the team on best practices.

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.

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.

Guard data integrity :
Own reconciliation, monitoring and alerting so that positions, prices and performance figures are right before they reach a portfolio or a client.

Qualifications

Senior Development Background:  8+ years of professional experience building and maintaining production-grade systems, ideally data-intensive or numerical applications.

Core Technical Proficiency:  Expert-level Python, SQL/PostgreSQL, data modeling, and API design.

AI-Native Development:  Deep, hands-on use of AI coding agents (e.g., Claude Code, Codex, Cursor) in your daily workflow, and the system-design judgment to direct them: decomposing work, supplying the right context, reviewing and testing their output, and knowing when not to trust it.

Quantitative Fluency:  Comfort with…
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