Senior Machine Learning Engineer

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.

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.

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