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 an ML engineer to join our data science team and help build our next generation of machine learning based solutions. The ideal candidate is passionate about building robust, scalable machine learning products with a strong focus on real-time system performance and observability.

Responsibilities

• Collaborate closely with data scientists to deliver high-quality, scalable ML models.
• Own the end-to-end machine learning lifecycle—from building efficient training and evaluation pipelines to deploying models in production environments.
• Develop and maintain monitoring and observability tools, including dashboards, alerts, and performance tracking for models in production.
• Promote engineering best practices including code and model versioning, CI/CD pipelines, rollout strategies, and disaster recovery procedures.

• 2+ years of experience as a Machine Learning Engineer
• 4+ years of experience as a software engineer
• Strong programming skills in Python and SQL
• Experience with ML tools and frameworks such as Sagemaker, Tensorflow, MLflow, Airflow
• Hands-on experience with MPP frameworks such as Spark, Flink, Ray, Dask or equivalent
• Strong analytical thinking and problem-solving skills.
• BSc in Computer Science, Engineering, Statistics, or a related technical field.

Work on all parts of the ML stack. Setting environments, building feature stores, experiments, operations and all the way to production.