Who We Are

Kensho is S&P Global’s hub for AI innovation and transformation. With expertise in Machine Learning and data discovery, we develop and deploy novel solutions for S&P Global and its customers worldwide. Our solutions help businesses harness the power of data and Artificial Intelligence to innovate and drive progress. Kensho's solutions and research focus on Generative AI, LLM Agents, speech recognition, entity linking, document extraction, text classification, natural language processing, and more.

At Kensho, we hire talented people and give them the autonomy and support needed to build amazing technology and products. We collaborate using our teammates' diverse perspectives to solve hard problems. Our communication with one another is open, honest, and efficient. We dedicate time and resources to explore new ideas, but always rooted in engineering best practices. As a result, we can innovate rapidly to produce technology that is scalable, robust, and useful.

About the Team

We are looking for a ML Engineer to join the group of Machine Learning Engineers working on developing a cutting-edge GenAI platform, LLM-powered applications, and fundamental AI toolkit solutions such as Kensho Link. We are looking for talented people who share our passion for bringing robust, scalable, and highly accurate ML solutions to production.

You can read about some of our cutting-edge GenAI application at: 

• S&P Global and Anthropic Announce Collaboration to Bring Trusted Financial Data into Claude 

• S&P Global Data Retrieval Agent for Gemini Enterprise | Google Cloud AI agent finder 

About The Role

As a ML Engineer, you will work across a broad set of machine learning areas at Kensho. This includes building retrieval-driven AI agents that ground LLM outputs in trusted S&P data, as well as developing and operating production-grade machine learning systems that power our essential AI toolkits like Kensho Link or NERD. Across these areas, your focus will be on creating reliable, scalable, and transparent ML solutions that deliver real value to users.

This role is ideal for engineers who:

• Enjoy building robust, production-grade ML systems end to end

• Have strong ML fundamentals, with a practical understanding of the backend and deployment work needed to run these systems in production

• Want to work across data, retrieval, modeling, and system design

You will partner with ML engineers, backend engineers, and product managers to drive the next generation of Kensho’s AI products.

Why Join Us? 

Kensho is expanding its portfolio of ML-powered products, and we are looking for engineers who are excited to build and deploy state-of-the-art ML systems at scale. You will have the opportunity to influence key architectural decisions, experiment with new approaches, and accelerate the pace at which we prototype, develop, and maintain high-impact ML solutions.

We are seeking a mid-to-senior level Machine Learning Engineer who can help elevate and streamline our ML development lifecycle, setting a high bar for excellence in modeling, deployment, and production operations.

What We Are Looking for-

• Bachelor's degree or higher in Computer Science, Engineering, or a related field.

• 3+ years of significant, hands-on industry experience with machine learning, natural language processing (NLP), and information retrieval systems, including designing, shipping, and maintaining production systems.

• Strong proficiency in Python.

• Experience reading and understanding SQL databases and writing queries for specific access patterns. 

• Proven experience building ML pipelines for data processing, training, inference, maintenance, evaluation, versioning, and experimentation.

• Demonstrated effective coding, documentation, collaboration, and communication habits.

• Strong problem-solving skills and a proactive approach to addressing challenges.

• Ability to adapt to a fast-paced and dynamic work environment.

• Experience working with machine learning libraries/frameworks for Large Language Model (LLM) orchestration, such as Langchain.

• (Preferred) Experience working with RAG based system 

What You’ll Do:

• Develop Advanced ML Systems: Create, refine, and deploy machine learning systems that solve complex business problems and power Kensho products.

• Build Retrieval-Driven AI Agents: Design AI agents that fetch, validate, and structure data from S&P datasets, ensuring answers produced by LLMs are grounded in S&P’s data universe.

• Evaluate LLM-based Agents: Identify and resolve performance gaps in both online and offline settings, addressing issues such as performance, latency, memory usage, compute efficiency, and feature consistency.

• Work With Domain Specific Data: Leverage proprietary structured and unstructured datasets, deep dive to have domain understanding, work with Subject Matter Experts (SMEs).

• Scale ML Applications: Optimize and scale ML systems to support high demand, efficient resource utilization, and reliable production behavior.

• Reduce Technical Debt: Proactively identify areas of the stack that can be improved, and propose solutions that strengthen reliability and maintainability.

• Taking Initiative: Scope, plan, and execute ML initiatives that develop core capabilities across Kensho products.

• Collaborate Across Teams: Work closely with Data, Product, Design, and Engineering teams to ensure smooth operations and contribute to long-term product vision.

• Improve User Experiences: Partner with Product and Design to develop ML-driven functionality that enhances user workflows and aligns with business needs.

• Drive the ML Lifecycle: Engage in all phases of the ML lifecycle, from problem framing and data exploration to model deployment and production monitoring, ensuring continuous improvement.

Technologies We Love:

• Traditional ML: Scikit-learn, XGBoost, LightGBM 

• ML/Deep Learning: PyTorch, Transformers, HuggingFace, LangChain

• Deployment tools such as: Docker, Amazon EKS, Jenkins, AWS

• EDA/Visualization: Pandas, Matplotlib, Jupyter, Weights & Biases

• Tools/Toolkits: DVC, MosaicML, NVIDIA NeMo, LabelBox

• Techniques: RAG, Prompt Engineering, Information Retrieval, Data Embedding

• Datastores: Postgres, OpenSearch, SQLite, S3