Machine Learning Engineering Manager

About the Role

Grade Level (for internal use)

03Who 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 Role

As a Machine Learning Engineering Manager, you will lead a team of ML Engineers and Applied ML Scientists developing Kensho’s GenAI platform, LLM-powered applications, and foundational AI toolkits like Kensho Link or NERD. You will guide the team in transforming advanced ML research into reliable, scalable, and production-ready systems used across S&P Global.

Your responsibilities span deep technical leadership, people management, and cross-functional collaboration. You will ensure your team is productive, supported, and delivering high-impact ML systems that align with product and business goals. While your primary focus is enabling your team’s success, you will remain close enough to the technical work to make informed decisions, mentor effectively, and contribute where your expertise adds value.

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

What we are looking for-

• Have 7+ years of industry experience designing, building, evaluating, and maintaining robust and scalable production ML systems
• Have 2+ years of experience managing ML engineering or applied ML teams
• Have experience mentoring engineers and scientists, with a long-term mindset toward team development and hiring
• Have partnered with product managers to define roadmaps, scope problems, and drive user-focused outcomes
• Have a deep understanding of modern ML system design, including data processing, training, retrieval, evaluation, deployment, and production monitoring
• Are comfortable leading technical decisions and guiding teams through complex modeling and system design trade-offs
• Are an effective communicator who can translate between engineering, ML, product, and business stakeholders
• Are innovation-minded and able to propose creative, practical solutions to ambiguous problems
• Are a collaborative reviewer and a thoughtful teammate who values clarity, feedback, and shared ownership
• Are highly organized, results-oriented, and capable of ensuring steady execution while supporting individual growth
• Measure your success through your team’s success and impact

What You’ll Do

• Lead and Grow a High-Performing ML Team: Manage, mentor, and develop a team of ML Engineers and Applied ML Scientists, ensuring they are engaged, supported, and set up for long-term success.
• Drive ML Strategy and Execution: Define technical direction, set priorities, and guide the team in building models, retrieval agents, and ML systems that power Kensho’s GenAI platform and AI toolkits such as Link and NERD.
• Deliver Production-Grade ML Systems: Ensure the team follows best practices for building robust, scalable, and maintainable ML solutions, including data pipelines, training workflows, retrieval systems, and model deployment.
• Advance Retrieval-Driven AI Agents: Oversee the development and evaluation of LLM-powered agents