Senior/Staff/Lead ML Engineer

Added: 3/15/2022

REF: 20469

Contract: Permanent

Location: - None Specified -, - None Specified -

We are focused on improving the global supply chain. Our purpose is to enable resilient, sustainable, secure, and inclusive global commerce: Globalization 2.0. 

We have built a knowledge graph of the global supply chain: a living map of trillions of dollars of commercial activity, covering over 400M companies connected by billions of shipments. This knowledge graph, after just 3 years since founding, is already used by many of the world’s leading governments, enterprises, and logistics providers.

We are looking for talented software engineers with experience with data products. You’ll work closely with our Data Scientists on projects to analyze and observe world-scale datasets, write code that can scale to produce never before seen insights, and construct APIs to deliver our product vision.

This position can be worked remotely, but you should be comfortable working on New York time.


  • Develop and deploy our Entity Resolution/Natural Language Clustering platform
  • Build and maintain distributed machine learning pipelines
  • Analyze and propose technical solutions to invent, enable, and enhance our product offerings
  • Be responsible for automating, testing, and deploying your work
  • Collaborate with fellow engineers and data scientists across the organization

About You

  • MS or PhD in Computer Science, Data Science, or equivalent experience
  • You have 5-10 years of real-world professional experience writing back end or data-driven software
  • You have a track record of ownership and delivery of projects with major organizational impact
  • You care deeply about engineering excellence, clean code, and knowledge-sharing
  • You have strong written and verbal communication skills

Nice to have, but not required

  • Experience with Python Machine Learning toolsets (Scikit-learn, Numpy, Pandas, Dedupe)
  • Experience with container technologies like Docker and Kubernetes
  • Working knowledge of cloud services like AWS, Azure, or GCP

Technologies we love

  • Languages: Python, Go, Java, Spark
  • Tools: Docker, Git, Kubernetes, Swagger/OpenAPI, AWS, Azure
  • Datastores: Databricks, Elasticsearch, Postgres, Redshift, Neo4j, ArangoDB


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