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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.

Build and improve Kensho’s ML platform, tooling, infrastructure, and workflows. The role supports ML engineers by developing scalable, auditable processes for model fine-tuning, reinforcement learning, LLM and agent evaluation, and production observability. Responsibilities include integrating open-source solutions, debugging distributed systems, monitoring model and agent performance, and promoting engineering best practices across teams. The position requires collaboration across ML, infrastructure, SRE, and security teams.

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, 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.