Build and deploy ML models for entity resolution and knowledge graph expansion on large-scale China-related data. Requires 4+ years clustering ML experience and end-to-end production ML with Python/SQL.
150k – 195k/yr
Hybrid4+ YOEML Engineering
About the role
What You'll Do
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
What we’re looking for
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
Nice to have
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)
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