AI Engineer

Come build what's next with Topsort!

About Topsort

Topsort is a global Commerce AI company making advanced advertising and AI-powered technology accessible to businesses of all sizes.

Our technology helps retailers and marketplaces grow through smarter advertising, real-time auctions, and intelligent product discovery. With a presence in 40+ countries and a growing international team, we're building the technology behind the next generation of commerce.

What Is This Role Like?

We're looking for an AI Engineer to join our T-Brain team and help build the intelligence powering the next generation of commerce.

T-Brain is Topsort's AI platform, developing machine learning models that power predictions, recommendations, and smarter decision-making across retail environments.

In this role, you'll work at the intersection of Machine Learning, Data Science, and Software Engineering, developing models, running experiments, and turning complex data into scalable, production-ready solutions.

What You'll Own

Machine Learning Development: Design, train, evaluate, and improve ML models for predictions, recommendations, and other commerce applications.

Experimentation & Validation: Design and run experiments, including A/B testing, to evaluate model performance and make statistically grounded decisions.

Production ML: Build, deploy, and maintain reliable ML solutions using cloud infrastructure, ensuring scalability and performance.

Data & Modeling: Work with complex datasets to identify patterns, develop predictive models, and improve model effectiveness.

Model Innovation: Contribute to the development of large-scale foundation models and explore how experimental findings can be applied across different use cases.

What Success Looks Like

Production-Ready Models: ML models are reliably deployed, maintained, and performing at scale in production environments.

Validated Improvements: Experiments demonstrate measurable improvements against established baselines, supported by sound statistical evidence.

Model Performance: Predictions and recommendations deliver consistent, measurable improvements in accuracy, relevance, or other defined performance metrics.

Scalable Solutions: Models and experimental findings can be adapted and extended across different clients and retail use cases.

Continuous Improvement: Monitoring, experiment results, and changing data patterns inform ongoing improvements in model performance and reliability.

What You'll Bring

Don't check every box? We'd still love to hear from you. We value curiosity, strong judgment, and people who take ownership of results:

Relevant Experience: 5+ years of experience in Machine Learning Engineering, Applied Machine Learning, or Data Science, with hands-on experience deploying models in production.

Academic Background: BSc. degree in Computer Science, Software Engineering, Mathematics, Statistics, or a related quantitative field .

Machine Learning: Strong experience developing, training, evaluating, and optimizing ML models for real-world applications.

Cloud ML: Hands-on experience with cloud platforms such as AWS, GCP, or Azure, including model training, deployment, and scalable ML pipelines.

Python & Data: Strong Python skills and experience with SQL, data processing, and working with large datasets.

Experimentation: Solid statistical foundations and practical experience designing, running, and interpreting A/B tests to evaluate model performance.

Software Engineering: Strong coding practices and experience building reliable, maintainable, production-ready ML systems.

Nice To Have:

Experience in e-commerce, Retail Media, AdTech, or personalization.

Experience with recommendation systems, search relevance, retrieval, or ranking models.

Familiarity with deep learning architectures, including neural networks and transformers, using frameworks such as PyTorch or TensorFlow.

Familiarity with emerging ML researc