Machine Learning Engineer, Core ML (Remote)

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What Brex is looking for in applicants

Why join us

Brex is reimagining financial systems so every growing company can realize their 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.

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.

Data at Brex

The Data organization develops infrastructure, statistical models, and products using financial data. Our Data Scientists and Engineers work together to make data—and insights derived from data—a core asset across the company. Our work is ingrained in Brex’s decision-making process, in the efficiency of our operations, in our risk management policies, and in the second-to-none experience we provide to our consumers. 

What you’ll do

As an MLE, you collaborate with other platform and infra teams, build systems and tools to support Brex-wide ML efforts and initiatives. These tools enable data scientists to improve the core data quality at Brex, to iterate faster in the model development cycle including model training, online productionization and performance monitoring, essentially to achieve their optimal performance. 


  • Design and build user-friendly and ML guided tools upon which data scientists can easily test and deploy models to production.
  • Apply your expertise in quantitative analysis and software engineering to build scalable and robust real-time ML models. 
  • Work closely with data scientists to adopt the most recent ML technologies and ensure the highest engineering quality. 
  • Collaborate with cross-functional teams to unravel complex problems by clearly formulating the problem statement, understanding technical requirements, and presenting findings at all levels. 
  • Maintain a strong data driven culture within the company by interacting with diverse internal functions.


  • 3+ years in a Data Science / ML Engineering/ Software Engineering role
  • Experience in software abstraction, scalable data application architecture for improving Data Scientist’s efficiency.
  • Experience in the machine learning model development cycles, including model productionization and management. 
  • Strong interpersonal and communication skills and having worked with different business functions.
  • Experience with data pipelines and data warehousing, such as Airflow and Snowflake or equivalent tools and frameworks.
  • Experience working with SQL or NoSQL databases 
  • Experience working with one or more backend programming languages (C++, Java, Kotlin, Python)

Bonus points

  • A MS or Ph.D. degree in a quantitative field (e.g. stats, physics, computer science)
  • Experience contributing to machine learning platforms for building data pipelines and models
  • Understands various tradeoffs in machine learning deployment such as computation optimization, performance requirements, latency and data quality assurance.
  • Experience in deploying/managing/interacting with microservices, event-streaming pipeline and data intensive applications.
  • Production experience with either Graphical Model, or Neural Networks or Tensorflow. 

Want some tips on how to get an interview at Brex?

What is Brex looking for?
If this role looks interesting to you, a great first step is to understand what excites you about the team, product or mission. Take your time thinking about this and then tell the team! Get in touch and communicate that passion.
What are interviews for Machine Learning Engineer like?
Interview processes vary by company, role and team. The best plan is to see what others have experienced and then plan accordingly.
How to land an interview at Machine Learning Engineer?
A great first step is organizing your path to an offer. Check out Kiter for tools to get started!