Machine Learning Engineer

Join WireScreen as a Machine Learning Engineer and work on unlocking the source of truth behind China's economy. You will be responsible for building models that support entity resolution and our knowledge graph, contributing to our existing MCP servers and agentic pipelines. You will work closely with engineering, product, and research teams to expand our AI toolkit and scale our data ingestion processes. This role requires significant experience with Python programming and SQL, as well as 4+ years of experience working on clustering-type ML problems.

• Contribuer à l'amélioration des algorithmes de résolution d'entités existants pour découvrir des connexions cachées entre des personnes et des organisations en Chine.
• Travailler avec l'équipe produit pour définir et mettre en œuvre des systèmes d'évaluation pour les systèmes d'apprentissage automatique classiques et agentiques.
• Former, tester et déployer des modèles d'apprentissage automatique qui fonctionnent sur des dizaines de millions d'enregistrements quotidiennement.
• Significant experience with python programming and SQL
• 4+ years of experience working on clustering-type ML problems, ideally in the domain of knowledge graphs / entity resolution, but other domains could include; recommendation engines, cohort analysis, outlier/anomaly detection
• End-to-end machine learning model experience in production; that you’ve stood up a service including experimenting, training, testing and tuning a job against a dataset all the way through to deployment and beyond. Model families could include clustering, classification/regression, dimensionality reduction and embeddings, nearest-neighbor/similarity methods (e.g. KNN, SVM), ensembles, NLP, and deep learning
• Experience working with Frontier/SOTA models and/or fine-tuning your own LLMs for specific tasks
• Working on problems across large, heterogeneous, messy unstructured datasets and/or with semantic search, computer vision (especially OCR), or linear optimization problems
• Experience with any of the following technologies: PySpark, Temporal, FastAPI, Scikit-learn, NumPy, Docker, Terraform, Kubernetes
• Early-stage startup experience (Series B or earlier)
• B2B SaaS experience