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AI Summary
About Trafilea
Trafilea is a Consumer Tech Platform for Transformative Brand Growth. We’re building the AI Growth Engine that powers the next generation of consumer brands.
With over $1B+ in cumulative revenue, 12M+ customers, and 500+ talents across 19 countries, we combine technology, growth marketing, and operational excellence to scale purpose-driven, digitally native brands.
We own and operate our own digitally native brands (not an agency), with presence in Walmart, Nordstrom, and Amazon, and a strong global D2C footprint.
Why Trafilea
We’re a tech-led eCommerce group scaling our own globally loved DTC brands, while helping ambitious talent grow just as fast.
🚀 We build and scale our own brands.
🦾 We invest in AI and automation like few others in eCom.
📈 We test fast, grow fast, and help you do the same.
🤝 Be part of a dynamic, diverse, and talented global team.
🌍 100% Remote, USD competitive salary, paid time off, and more.
Key responsibilities
As a Sr. Machine Learning Engineer, you’ll lead the development and deployment of advanced ML models and scalable systems that power critical business decisions.
You will:
Develop production-ready machine learning systems with robust testing and scalable architecture
Build and improve forecasting and optimization models using XGBoost and advanced ML techniques
Design data pipelines and ML workflows that transform raw data into business intelligence
Bring research into production through experimentation, iteration, and deployment
Integrate ML systems into real business operations and user-facing tools
Drive model explainability initiatives using SHAP values and LLM-powered insights
Collaborate closely with Growth, Marketing Science, Product, and Engineering teams
Influence technical strategy while aligning ML initiatives with business impact
Mentor team members and help elevate engineering and ML standards across the organization
5+ years of experience building and deploying machine learning systems in production
Strong expertise in Python, SQL, and modern ML frameworks
Experience with XGBoost, forecasting models, and scalable ML pipelines
Deep understanding of MLOps, testing strategies, CI/CD, and data engineering practices
Experience working with AWS ecosystem, MLFlow, or similar cloud ML tooling
Strong knowledge of the ML lifecycle, experimentation frameworks, and model optimization
Ability to communicate technical concepts clearly to both technical and business stakeholders
Experience in growth, marketing, e-commerce, or high-scale digital environments is a strong plus
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