Machine Learning Engineer, Data Products (Remote)

Machine Learning at Brex

The Data Science ML teams build automated data-driven solutions with machine learning & AI to help optimize business decisions for internal customers and to empower and delight our growing external customers, while protecting Brex. We are fully embedded into our business functions to drive towards the same goals, while also keeping a data science community where we learn from each other and collaborate on various DS initiatives.  

What you’ll do

As an MLE on the Data Products team, you will collaborate with our Engineering, Products, and Design teams to identify customer pain points, develop and deploy machine learning models to convert them to delightful customer experiences. 

Responsibilities

• Apply your expertise in quantitative analysis, machine learning, and software engineering to build scalable and robust batch and real-time ML models from end to end.
• 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.

Requirements

• 4+ years in a Data Science / ML Engineering/ Software Engineering role
• Experience in the end to end machine learning model development cycles, including model productionization and management. 
• Strong interpersonal and communication skills and having worked with different business functions.
• Proficiency in Python, and/or other backend programming languages (C++, Java, Kotlin,)
• Proficiency in SQL or NoSQL databases 
• Experience with data pipelines and data warehousing, such as Airflow and Snowflake or equivalent tools and frameworks.

Bonus points

• A MS or Ph.D. degree in a quantitative field (e.g. stats, physics, computer science)
• Understands various tradeoffs in machine learning deployment such as computation optimization, performance requirements, latency and data quality assurance.
• Experience with deep learning (NLP, computer vision, etc.) 
• Experience in deploying/managing/interacting with microservices, event-streaming pipeline and data intensive applications.