Senior Software Engineer, Risk at Brex Inc.
San Francisco, CA 94199
About the Job
Brex is the AI-powered spend platform. We help companies spend with confidence with integrated corporate cards, banking, and global payments, plus intuitive software for travel and expenses. Tens of thousands of companies from startups to enterprises — including DoorDash, Flexport, and Compass — use Brex to proactively control spend, reduce costs, and increase efficiency on a global scale.
Working at Brex allows you to push your limits, challenge the status quo, and collaborate with some of the brightest minds in the industry. We’re committed to building a diverse team and inclusive culture and believe your potential should only be limited by how big you can dream. We make this a reality by empowering you with the tools, resources, and support you need to grow your career.
Brex is reimagining financial systems so every growing company can realize its full potential. As the financial OS, we’re building software and services in one place—disrupting long-entrenched institutions with products and experiences that better serve the ambitions of our customers.
Engineering at Brex
The Engineering team includes Data, IT, Security, and Software, and is responsible for building innovative products and infrastructure for Brex and our customers. We believe that engineers should accelerate the business through technology, and collaborate across multiple teams to accomplish that.
Teams are autonomous, value inclusivity, eager to learn, teach, and constantly improve how things work. The software we build today is the foundation for dozens of Brex systems in the future, so engineers have a strong sense of ownership and accountability and take pride in their craft.
What you’ll do
We are seeking individuals passionate about building data-driven systems to manage risk at scale. You will have significant exposure to every aspect of financial crime prevention, from building customer facing experiences to working closely with data scientists to seamlessly integrate models into production. As Risk services interface with multiple systems across Brex, excellent interpersonal and written communication skills are essential. You will be working on high-impact projects requiring high development standards, handling and upkeep integrity of sensitive data, and ensuring compliance while driving monetary outcomes. In this role, you’ll gain specialized knowledge in risk management and contribute to building the most beloved financial services and softwares, all while safeguarding our customers, our platform, and the integrity of the financial market.
Responsibilities
- You'll be responsible for ensuring that the data for our decisioning systems is always of the highest quality, utilizing best-in-class services (risk vendors and internal models) to provide actionable insights that drive risk management and customer experience improvements.
- Develop low-latency decision systems for assessing risk during customer onboarding, payments, authentication, and in-app.
- Build in-product levers that enhance our decisioning systems while striving to make risk management invisible.
- Building systems and tools for comprehensive customer due diligence, including KYC, sanctions, adverse media, PEP, and AML monitoring.
Requirements
- 4+ years of professional experience in a software engineering role or equivalent experience.
- Strong fundamental knowledge in at least one of the following: machine learning theory or systems.
- Experience building and designing scalable decisioning systems.
- Experience working with backend programming languages (Kotlin, Python).
- Ability to hold yourself and the team to high standards.
- Strong communication and interpersonal skills.
- Must be willing to work in office 2 days per week on Wednesday and Thursday.
Bonus Requirements
- Publications or contributions to open-source projects related to data-intensive systems.
- Familiarity with algorithms for decision-making under uncertainty, including Bayesian networks, Markov decision processes, or reinforcement learning.