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

18 Days AgoSaved
Hybrid
Cambridge, MA, USA
Artificial Intelligence • Fintech • Machine Learning • Natural Language Processing • Software • Generative AI
Build and improve ML platform tooling, infrastructure, workflows, and observability for machine learning models, LLMs, and agentic applications. Collaborate with ML and infrastructure teams, evaluate and integrate open-source tools, automate fine-tuning and evaluation pipelines, and debug distributed systems across infrastructure, networking, and application layers.
2 Months AgoSaved
Hybrid
Cambridge, MA, USA
Artificial Intelligence • Fintech • Machine Learning • Natural Language Processing • Software • Generative AI
Own AI partnership development from qualification through launch while managing strategic customer accounts. Build partner relationships, coordinate cross-functional sales, product, engineering, legal, and marketing efforts, and drive commercial outcomes. Lead customer adoption, renewals, expansion, trial conversion, and time-to-value for API, enterprise, and developer products. Operate autonomously in ambiguous environments, create new playbooks, resolve engagement issues, and manage senior stakeholder relationships globally.
2 Months AgoSaved
Hybrid
Cambridge, MA, USA
Artificial Intelligence • Fintech • Machine Learning • Natural Language Processing • Software • Generative AI
Design, implement, and scale end-to-end retrieval-augmented generation (RAG) pipelines. Build and optimize retrieval systems, embeddings, vector search, and LLM orchestration. Collaborate with product, design, and MLOps to productionize robust ML systems across large proprietary datasets.