Machine Learning Engineer
Job Overview
Moburst is a full-service, mobile-first digital agency that propels companies into category leaders through AI-powered marketing. Trusted by global brands from startups to enterprises like Microsoft, Sony PlayStation, we deliver end-to-end results across the full marketing stack. Driven by our proprietary AI technologies, we combine advanced data analytics, automation, and predictive modeling to pioneer intelligent solutions that power unparalleled client growth.
We are looking for a hands-on Machine Learning and Predictive Modeling Engineer with experience building, training, testing, and improving prediction models using real-world data.
The ideal candidate understands different types of machine learning and statistical prediction models, knows how they work, and can determine which model is most suitable for each use case. This person should be comfortable working independently, testing models at scale, analyzing results, and turning experiments into reliable production solutions.
The person will work across different company projects and products based on evolving business needs. Some initiatives focus on predictive modeling and machine learning, while others may involve broader technical or AI-related challenges. The role requires someone who can take full ownership of a project or product, from understanding the need and defining the solution to execution, testing, delivery, and ongoing improvement.
Responsibilities
Machine Learning & Data Operations
Model Lifecycle: Research, select, train, fine-tune, and evaluate ML and statistical models tailored to business needs.
Data Preparation: Clean, structure, and validate complex datasets for robust training and testing.
Experimentation at Scale: Run automated bulk experiments, track performance metrics, and optimize model accuracy.
Engineering & Ownership
End-to-End Delivery: Take full ownership of technical solutions- from requirements and Python development to deployment and maintenance.
System Quality: Write scalable, production-ready Python code while leveraging AI coding tools to boost velocity without compromising architecture.
Cross-Functional Execution: Collaborate remotely with product and dev teams, manage priorities effectively, and communicate risks early.
Requirements
ML & Stats Expertise: Proven track record in training, evaluating, and improving ML/statistical models on real-world datasets.
Production-Grade Python: Strong Python skills with a focus on writing clean, scalable, and maintainable code.
Smart AI Integration: Proficiency using AI coding tools to accelerate output without sacrificing core engineering fundamentals.
End-to-End Execution: Ability to take full ownership, solve complex analytical problems, and deliver independently in a remote environment.
Communication: Strong written and spoken English for seamless remote collaboration.