Machine Learning Engineer
Copia Wealth Studios is looking for Machine Learning Engineer to tackle the radical transformation underway in the finance space.
We have four core values that we believe make the ideal teammate:
Our first Product is a financial intelligence platform designed to help high net worth individuals (think business owners, family offices, entrepreneurs, and athletes) manage their investments. Our 'Patrons' track everything from art to crypto to real estate and business assets.
The team comprises veterans who have been shipping products for decades; we've been designing and developing native apps since the dawn of the iOS SDK. We've already unlocked early product-market fit, we're fully funded, and we have plenty of runway to match our ambitions.
We don't believe in code tests, but if you're interested in helping us build the future of finance, send a few samples of your work: GitHub, radars on Open Radar, or something else that represents work your proudest work.
You love solving challenging problems and building robust features within cross-functional teams. You want opportunities to shape best practices, challenge product assumptions, and ship world-class software.
What are some exciting challenges with the role?
The nature of our Product means we have to consider a ton of integrations. We have to connect to banks, investment platforms, institutional systems, and the like. Of course, the back end handles the heaviest lifting, but it does create unique UI/UX challenges as each user can have a unique perspective about how we present information.
We're also trying to create some very novel finance 'widgets' that will help non-financial users understand complex portfolios. For example, an "Efficient Frontier" that uses Monte Carlo simulations to run various portfolio scenarios. Another example would be our "fruit tree," a digital tree that bears more fruit as your investments mature.
And perhaps most crucially, we're building a document ingestion engine that requires creating several new user interaction paradigms.
What is the structure of our engineering team?
Our engineering team operates in 2-week sprints. Everyone on the team contributes regularly, and pull requests are a core part of life. Everyone has a direct impact on the Product and is actively involved in critical product decisions. We're using Clubhouse.io to manage the backlog from a process perspective, and we have regular sprint planning sessions and a long-term roadmap.
We believe in continuous integration and are all about functional programming. The team is very senior and open to fostering a supportive growth-minded work environment. We're using SwiftUI for the front, which is still bleeding edge for many folks. The back end is built almost entirely in Elixir.
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