AI Engineer

<div class="content-intro"><p><strong>Come build what's next with Topsort!</strong></p></div><p><strong>About Topsort</strong></p> <p>Topsort is a global Commerce AI company making advanced advertising and AI-powered technology accessible to businesses of all sizes.</p> <p>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.</p> <p><strong>What Is This Role Like?</strong></p> <p data-pm-slice="1 1 []">We're looking for an <strong>AI Engineer</strong> to join our T-Brain team and help build the intelligence powering the next generation of commerce.<br><br>T-Brain is Topsort's AI platform, developing machine learning models that power predictions, recommendations, and smarter decision-making across retail environments.<br><br>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.</p> <p><strong>What You'll Own</strong></p> <ul> <li><strong>Machine Learning Development:</strong> Design, train, evaluate, and improve ML models for predictions, recommendations, and other commerce applications.</li> <li><strong>Experimentation & Validation:</strong> Design and run experiments, including A/B testing, to evaluate model performance and make statistically grounded decisions.</li> <li><strong>Production ML:</strong> Build, deploy, and maintain reliable ML solutions using cloud infrastructure, ensuring scalability and performance.</li> <li><strong>Data & Modeling:</strong> Work with complex datasets to identify patterns, develop predictive models, and improve model effectiveness.</li> <li><strong>Model Innovation:</strong> Contribute to the development of large-scale foundation models and explore how experimental findings can be applied across different use cases.</li> </ul> <h4>What Success Looks Like</h4> <ul> <li><strong>Production-Ready Models: </strong>ML models are reliably deployed, maintained, and performing at scale in production environments.</li> <li><strong>Validated Improvements: </strong>Experiments demonstrate measurable improvements against established baselines, supported by sound statistical evidence.</li> <li><strong>Model Performance: </strong>Predictions and recommendations deliver consistent, measurable improvements in accuracy, relevance, or other defined performance metrics.</li> <li><strong>Scalable Solutions: </strong>Models and experimental findings can be adapted and extended across different clients and retail use cases.</li> <li><strong>Continuous Improvement: </strong>Monitoring, experiment results, and changing data patterns inform ongoing improvements in model performance and reliability.</li> </ul> <p><strong>What You'll Bring</strong></p> <p><em>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:</em></p> <ul> <li><strong>Relevant Experience: </strong>5+ years of experience in Machine Learning Engineering, Applied Machine Learning, or Data Science, with hands-on experience deploying models in production.</li> <li><strong>Academic Background:</strong> BSc. degree in Computer Science, Software Engineering, Mathematics, Statistics, or a related quantitative field<strong>.</strong></li> <li><strong>Machine Learning: </strong>Strong experience developing, training, evaluating, and optimizing ML models for real-world applications.</li> <li><strong>Cloud ML: </strong>Hands-on experience with cloud platforms such as AWS, GCP, or Azure, including model training, deployment, and scalable ML pipelines.</li> <li><strong>Python & Data: </strong>Strong Python skills and experience with SQL, data processing, and working with large datasets.</li> <li><strong>Experimentation: </strong>Solid statistical foundations and practical experience designing, running, and interpreting A/B tests to evaluate model performance.</li> <li><strong>Software Engineering: </strong>Strong coding practices and experience building reliable, maintainable, production-ready ML systems.</li> </ul> <p><strong>Nice To Have:</strong></p> <ul> <li>Experience in e-commerce, Retail Media, AdTech, or personalization.</li> <li>Experience with recommendation systems, search relevance, retrieval, or ranking models.</li> <li>Familiarity with deep learning architectures, including neural networks and transformers, using frameworks such as PyTorch or TensorFlow.</li> <li>Familiarity with emerging ML research and experience applying new techniques to practical problems.</li> </ul> <div class="p-rich_text_section"> <div class="p-rich_text_section"> <p><strong>Who Thrives Here</strong></p> <p><em>We set a high bar for engineering because the systems we build matter. You'll thrive here if you:</em></p> <ul> <li><strong>Ownership & Quality:</strong> You take responsibility for what you build, write producti