Machine Learning Engineer
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.
About the job
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)
- B2B SaaS experience
Skills
Python, SQL, Machine Learning, Entity Resolution, Knowledge Graphs, Clustering, NLP, scikit-learn, NumPy, Pyspark, FastAPI, Docker, Kubernetes, Terraform, Temporal
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