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:
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Develop production-ready machine learning systems with robust testing and scalable architecture
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Build and improve forecasting and optimization models using XGBoost and advanced ML techniques
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Design data pipelines and ML workflows that transform raw data into business intelligence
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Bring research into production through experimentation, iteration, and deployment
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Integrate ML systems into real business operations and user-facing tools
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Drive model explainability initiatives using SHAP values and LLM-powered insights
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Collaborate closely with Growth, Marketing Science, Product, and Engineering teams
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Influence technical strategy while aligning ML initiatives with business impact
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Mentor team members and help elevate engineering and ML standards across the organization
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5+ years of experience building and deploying machine learning systems in production
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Strong expertise in Python, SQL, and modern ML frameworks
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Experience with XGBoost, forecasting models, and scalable ML pipelines
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Deep understanding of MLOps, testing strategies, CI/CD, and data engineering practices
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Experience working with AWS ecosystem, MLFlow, or similar cloud ML tooling
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Strong knowledge of the ML lifecycle, experimentation frameworks, and model optimization
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Ability to communicate technical concepts clearly to both technical and business stakeholders
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Experience in growth, marketing, e-commerce, or high-scale digital environments is a strong plus
