Kensho Technologies

HQ
Cambridge
Total Offices: 2
175 Total Employees
Year Founded: 2013

Kensho Technologies Offices

Kensho Technologies is headquartered in Cambridge and has 2 office locations.

Hybrid Workplace

Employees engage in a combination of remote and on-site work.

Typical time on-site: Not Specified

U.S. Office Locations

HQ

Cambridge

Kensho is located in the heart of Harvard Square within walking distance to shops, restaurants, bars and public transportation.

New York

55 Water Street, New York, NY, United States, 10038

15 Days AgoSaved
Hybrid
New York, NY, USA
Artificial Intelligence • Fintech • Machine Learning • Natural Language Processing • Software • Generative AI
Software Engineering interns contribute to production features, applications, APIs, services, data ingestion pipelines, and machine-learning-enabled products. They participate in code reviews, deployment, observability, infrastructure as code, and production-readiness practices while collaborating with experienced engineers. Interns work in person from the Cambridge headquarters or New York City office and receive opportunities for technical learning, exploration, and cross-team development.
15 Days AgoSaved
Hybrid
New York, NY, USA
Artificial Intelligence • Fintech • Machine Learning • Natural Language Processing • Software • Generative AI
Develop and test machine learning models, pipelines, agentic systems, and LLM-powered applications under senior-engineer mentorship. Interns will work across the ML lifecycle, including problem framing, model selection, pipeline development, evaluation, and deployment support. The role involves collaborating with ML Engineers, Product Managers, Designers, and Full-Stack Engineers on scalable AI solutions.
15 Days AgoSaved
Hybrid
Cambridge, MA, USA
Artificial Intelligence • Fintech • Machine Learning • Natural Language Processing • Software • Generative AI
Build and evaluate machine learning models, agentic systems, LLM applications, and pipeline components under senior-engineer guidance. Interns will gain experience across the ML lifecycle, including problem framing, model selection, development, evaluation, and deployment support. The role involves collaborating with ML Engineers, Product Managers, Designers, and Full-Stack Engineers on scalable AI solutions.