Machine Learning Engineer, Applied Scientist - Knowledge Graph
San Francisco, CA
At Instacart, we manage catalog data imported from many retailers. We build product and knowledge graphs on top of the catalog data to support a wide range of applications including search and ads. It is fair to say that knowledge graphs are at the core of the e-commerce business. We're looking for experienced applied scientists and ML engineers to join our fast-moving team to work on knowledge bases to improve customer experience.
ABOUT THE JOB
There is tremendous opportunity in front of us, and joining now gives you a chance to grow your career and interests as we succeed.
- You will work closely with related teams, including Search, Ads, Catalog, Fraud Detection & Prevention, etc. You will have tremendous ownership and responsibility for managing things directly.
- You will be an active member of an internal community, including data engineers, data analysts, machine learning engineers, taxonomists, and economists sharing learnings, best practices and research across many domains.
- You will have the opportunities to work with academia through Ph.D. interns and faculties under joint research programs, and also publish in knowledge graph and other related domains.
- You will work on using data management, machine learning, and statistical inferencing techniques to solve problems of data integration and data quality for e-commerce catalog data.
- You will use theories of concepts and ontologies, natural language processing, graph algorithms, and information extraction techniques to create knowledge graphs and its supporting infrastructure.
- A graduate degree in computer science.
- 4+ years of academic or industry experience in knowledge graphs, data management, natural language processing.
- Strong programming skills (Python, C++) and data engineering skills (SQL, Pandas).
- Background in graph algorithms, and machine learning (e.g., XGBoost, Keras/Tensorflow).
- An ability to identify and prioritize high-impact problems and deliver solutions that provide reasonable trade-offs between urgency and quality.
- Self-motivation and a strong sense of ownership