Lead Machine Learning Engineer

Machine Learning (ML) and Deep Learning (DL) are the core of our product and data is lifeblood for all of our decision making. We are seeking a Lead Machine Learning Engineer (Lead MLE) to spearhead the design, development, and deployment of ML/DL models into production. As a Lead Machine Learning Engineer, you will own the end-to-end lifecycle of machine and deep learning systems, from model deployment and monitoring, to retraining, governance, and reliability in production. You will define the standards, tooling, and architectural patterns that allow data scientists and analysts to safely and efficiently ship models that directly power our credit and business decisions.

Requirements

Background:

• You have at least five (5) years of experience with machine and deep learning engineering in a practical setting.
• You have a good understanding of fintech products, and risk management to interpret business data effectively.

Technical expertise:

• You have strong programming abilities (structured, object-oriented, and/or event-oriented programming) and are comfortable programming in Python/R and SQL (with a focus on Snowflake, preferably).
• You have strong proficiency in ML/DL frameworks in Python (e.g. Tensorflow, PyTorch, Scikit-learn).
• You are comfortable consuming data through APIs, SFTP, or straight-up CSVs.
• You are experienced with MLOps tools (e.g. MLflow, Kubeflow, Docker, Kubernetes, AWS microservices).
• You have a solid understanding of cloud platforms, preferably AWS, distributed computing, and version control using GitHub & GitLab.
• You have a strong understanding of model serving patterns (batch vs. online, synchronous vs. asynchronous).
• You have experience designing feature pipelines with clear ownership, freshness guarantees, and backfills.
• You understand data engineering practices for ETL pipelines development, and datawarehouses/datalakes management.

Leadership & Business acumen:

• You have a data-oriented mindset: you care about getting to the bottom of how to make decisions based on data.
• You have stakeholder management experience, keeping everyone up-to-date with key findings and explaining in a non-technical way results, methodologies and processes for data-driven decision making.

Benefits

• Remote Work
• Contractor agreement
• PTOS