Machine Learning Engineering Manager

This job is with S&P Global, an inclusive employer and a member of my Gwork – the largest global platform for the LGBTQ+ business community. Please do not contact the recruiter directly.

About the Role:

Grade Level (for internal use):
03   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 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:
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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 and grounded retrieval systems that use trusted S&P datasets to produce accurate, verifiable results.

Shape Product and ML Roadmaps   :  Collaborate closely with Product Management and cross-functional leaders to identify opportunities, define problem statements, and align ML initiatives with business objectives.

Promote Engineering Excellence   :  Establish strong engineering practices, maintain high code quality, and foster a culture of reliability, observability, and continuous improvement across ML systems.

Hire and Scale the Team   :  Partner with Talent Acquisition to attract, interview, and onboard exceptional ML engineering talent as the ML organization grows.

Stay Hands-On Where It Matters   :  Contribute technically in design reviews, code…