Machine Learning Engineer

Description

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 scale well beyond many of the largest Internet companies, processing over 50 TB of raw data per day, we handle over 4 million requests per second, and interact with over a billion unique users a week. 

Our innovative platform is cutting-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 experienced engineer focusing on observability and ALM or data sciences to promote machine learning engineering. The person is passionate about building high-quality data products and processes, as well as supporting production real-time performance observability. 

As a Machine Learning Engineer at Bigabid, you will be handling Data Science projects from the preparation stage until production and beyond. You’ll be coordinating with stakeholders and play a major role in driving the business by promoting the models performance and lifecycle processes, which are the core of our product.

Responsibilities:

• Data wrangling - supporting and building data requirements for data science research as well as model training, validation and testing.

• Delivering end-to-end ML products - model performance development, training, validation, and testing, as well as version control.

• Promote engineering best practices - code and model versioning, CI/CD processes, rollout and DRP procedures.

• Create monitors, alerts, and dashboards - managing model performance in production.

• Collaborate with our product, data science, and engineering teams to solve problems and identify trends and opportunities.

• Implementation and POCs of open source tools and frameworks for MLE/MLOps and parallel processing.

Requirements

• 3+ years experience as a software engineer

• High level programming skills in Python and SQL

• Experience working with Spark & Airflow frameworks on large datasets

• Experience with machine learning pipelines - an advantage

• Experience as a backend engineer or devops engineer - an advantage

Excerpt

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