Jobs at Kensho Technologies

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.

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.

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.

Software engineering interns contribute to production features across Kensho teams, including machine-learning applications, APIs, services, platform abstractions, observability, deployment, and data ingestion systems. Interns participate in code reviews, production deployments, infrastructure as code, testing, documentation, and continuous learning while collaborating with experienced engineers. The role requires algorithm and data-structure knowledge, at least one technical area of expertise, adaptability, and practical projects or open-source experience.

Own AI partnership development and a portfolio of strategic clients. Manage partner relationships, coordinate cross-functional launches, drive product adoption and expansion, convert trials, support renewals, and resolve partnership or client issues. The role requires technical fluency in AI/ML, APIs, and software implementation, along with strong communication, senior relationship management, commercial judgment, and comfort operating autonomously in ambiguous environments. Approximately 25% travel is required within the United States and to company hubs.

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.

Design, build, and productionize agentic AI systems including LLM orchestration, retrieval, and evaluation. Work across the ML lifecycle from experimentation to deployment and monitoring, leverage proprietary structured and unstructured financial data, and collaborate with data, product, design, and MLOps teams to deliver scalable, robust agentic solutions.

Design, build, and productionize agentic AI systems and retrieval pipelines for financial data. Participate across the ML lifecycle: framing, experimentation, deployment, monitoring, and evaluation. Collaborate with cross-functional teams to integrate proprietary structured and unstructured data, improve agent performance, and automate MLOps workflows for scalable, robust AI-driven products.

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.

Design, build, and scale production RAG systems and retrieval pipelines integrating chunking, embeddings, vector databases, and LLM orchestration. Optimize vector search, indexing, and evaluation; collaborate with product, design, and MLOps to deploy and maintain end-to-end ML solutions.

Drive go-to-market strategy and adoption for Kensho AI products by bridging technical AI capabilities and commercial teams. Lead customer demos, technical deep-dives, co-development partnerships, market analysis, and sales enablement while feeding product and strategy teams with market insights to expand revenue and partnerships.

Drive market expansion and adoption of Kensho's AI products by bridging technical AI capabilities and commercial strategy. Lead go-to-market planning, customer demos, co-development partnerships, market analysis, sales enablement, and cross-functional alignment to translate customer needs into product and roadmap priorities.

Design and build AI-centric full‑stack TypeScript web applications (SPAs and APIs). Collaborate on product and architecture, own technical decisions, mentor peers, perform code reviews, and manage project priorities and deliverables.

The Senior Backend Software Engineer develops secure APIs utilizing Kensho's AI capabilities, collaborates across teams, and mentors colleagues while managing project deadlines and deliverables.

As a Senior Backend Engineer, you will develop APIs, collaborate with various teams, and manage project priorities while ensuring communication is efficient and inclusive.

As a Senior Full-Stack Software Engineer, you will build AI-centric web applications in TypeScript, manage project priorities, and make technology decisions while collaborating with Product and Design teams.

As an MLOps Engineer II, you'll enhance ML workflows, support engineers, improve model deployment processes, and advocate for best practices across teams.

As an MLOps Engineer at Kensho, you will develop tools and processes for ML workflows, assist ML engineers, and drive adoption of best practices across teams.