Machine Learning Engineer, WireScreen

• 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
• Significant experience with python programming and SQL

Desirable

• As a Machine Learning Engineer at WireScreen, you will be working across our data systems to unlock the source of truth behind one of the world’s economic powerhouses: China
• This role is critical to building models that support the two fundamental pieces of our business: entity resolution and our knowledge graph
• These systems are the cornerstones that uncover hidden connections within the Chinese economy
• You will be contributing to our existing MCP servers and agentic pipelines to classify, cluster and enrich across millions of entities
• You will be working with engineering, product and research to expand our AI toolkit, scaling our data ingestion processes, building analytics and insights into our platform at a scale not previously achievable
• Reporting directly to the VP of Engineering, you will work with our data team, enrichment team and product to establish our source of truth and expand our machine learning capabilities
• Fine tune our existing entity resolution algorithms to uncover hidden connections between people and organizations across China
• Expand our knowledge graph with alternative data to map out the power structure of China
• Train, test and deploy ML models that operate on tens of millions of records daily
• Work with Product to define and implement evaluation harnesses for classical ML and agentic systems
• Build agent workflows into internal tools to improve the scale and speed of our Research team