Careers at Bigabid

Bigabid focuses on solving the key challenge of growth for mobile apps by building Machine Learning and Big Data-driven technology that can both accurately predict what apps a user will like and connect them in a compelling way. 

Our technology operates at a scale well beyond many of the largest Internet companies, processing over 50 TB of raw data per day, handling over 4 million requests per second, and interacting with over a billion unique users a week. 

Our innovative platform is leading-edge with a strong product-market fit. As a result, we're seeing remarkable growth and close to zero customer churn. To support our hyper-growth and continue propelling the growth of some of the biggest names in the mobile industry, we offer a wide range of opportunities for different skill levels and experiences.

We are looking for a Machine Learning Tech Lead to promote machine learning engineering excellence. Someone who is passionate about building scalable, high-quality data products and processes, while ensuring production systems maintain strong real-time performance observability.

As a Tech Lead on the ML team at Bigabid, you will focus on designing and maintaining the core infrastructure that empowers the Machine Learning Engineers working within Data Science product teams. You’ll collaborate closely with stakeholders across data science, product, and engineering, playing a pivotal role in driving the business by architecting and enabling the infrastructure for machine learning model development, serving, and lifecycle management—the foundation of our product.

Responsibilities:

• Partner with MLEs in Data Science product teams and key stakeholders to design and maintain infrastructure for:
• Data wrangling – supporting and enabling data requirements for research, training, validation, and testing.
• End-to-end ML delivery – enabling model performance development, training, validation, testing, and version control.
• Drive engineering best practices including code and model versioning, CI/CD pipelines, rollout strategies, and disaster recovery procedures.
• Build and support monitoring and observability tools – dashboards, alerts, and performance tracking of models in production.
• Lead architecture projects such as:
• Feature Store – centralizing feature engineering and serving across teams.
• Vector Databases – enabling large-scale embedding storage and retrieval for advanced ML applications.
• GPU Cluster Scaling – optimizing distributed training and inference infrastructure.
• Collaborate with product, data science, and engineering teams to solve complex problems, identify trends, and create opportunities through robust ML infrastructure.