Staff Machine Learning Engineer (Search Ranking)

Join Snap Inc. as a Staff Machine Learning Engineer, where you will lead the development of next-generation search ranking systems. You will design, build, and improve machine learning models that determine the relevance, quality, personalization, and utility of search results at scale. You will own major ranking initiatives, develop and improve ranking models, build ranking systems that balance multiple objectives, and provide technical leadership across teams.

• Concevoir, construire et améliorer des modèles d'apprentissage automatique pour déterminer la pertinence, la qualité, la personnalisation et l'utilité des résultats de recherche à grande échelle.
• Diriger la conception et le développement de modèles d'apprentissage automatique pour le classement des recherches, y compris le classement de la pertinence, la personnalisation, la qualité des résultats, la compréhension de l'intention et l'optimisation de l'engagement.
• Analyser le comportement des utilisateurs, les journaux de recherche, les interactions requête-résultat et les performances des modèles pour identifier les opportunités d'amélioration.
• Excellent communication skills and ability to explain complex ML concepts to technical and non-technical stakeholders
• Experience with large-scale data processing and ML infrastructure, such as Spark, Flink, Beam, TensorFlow, PyTorch, JAX, or similar tools
• Bachelor's Degree in a relevant technical field such as computer science or equivalent years of practical work experience
• Strong machine learning fundamentals, including supervised learning, ranking models, embeddings, deep learning, optimization, evaluation, and experimentation
• Experience developing machine learning models for relevance ranking, personalization, intent understanding, and/or engagement optimization
• Strong programming skills in Python, C++, Java, Scala, or similar languages
• Strong understanding of online experimentation, A/B testing, metric design, model debugging, and tradeoff analysis
• Proven ability to lead complex technical projects across multiple teams
• Ability to take ML models from research or prototyping into large-scale production systems
• 8+ years of post-Bachelor’s machine learning experience; or Master’s degree in a technical field + 7+ year of post-grad machine learning experience; or PhD in a relevant technical field + 4 years of post-grad machine learning experience
• Direct experience building Search ranking systems, including query understanding, retrieval, ranking, re-ranking, relevance modeling, or result blending
• Advanced degree in Computer Science, Machine Learning, Statistics, Mathematics, Information Retrieval, or a related field
• Experience with ads ranking, recommendation ranking, feed ranking, marketplace ranking, or content discovery systems
• Experience with learning-to-rank methods such as LambdaMART, pairwise/listwise ranking losses, neural ranking models, or transformer-based rankers
• Experience with candidate generation, retrieval models, ANN search, embeddings, vector search, or two-stage ranking architectures
• Experience optimizing ranking systems for multiple objectives, including relevance, engagement, quality, diversity, freshness, long-term user value, and monetization
• Experience with LLMs, foundation models, semantic search, natural language understanding, or retrieval-augmented generation
• Experience building low-latency ML serving systems and improving production model reliability
• Track record of publishing, patenting, or otherwise advancing the state of the art in search, ranking, recommendations, ads, or applied ML