Machine Learning Engineering Manager
S&P Global
About the role
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 applications 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
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 reviews, modeling decisions, and architecture discussions, while empowering the team to own implementation and execution.
- Ensure Operational Stability: Oversee monitoring, debugging, and performance evaluation of ML systems in production, ensuring reliability and consistent service quality.
- Foster Collaboration Across Kensho: Work with Backend, Infrastructure, Product, and Data teams to ensure ML systems integrate seamlessly into Kensho's broader platform and applications.
Technologies We Love
- Traditional ML: SKLearn, 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
What's In It For You
Our Mission
Advancing Essential Intelligence.
Our People
We're more than 35,000 strong worldwide—so we're able to understand nuances while having a broad perspective. Our team is driven by curiosity and a shared belief that Essential Intelligence can help build a more prosperous future for us all. From finding new ways to measure sustainability to analyzing energy transition across the supply chain to building workflow solutions that make it easy to tap into insight and apply it, we are changing the way people see things and empowering them to make an impact on the world we live in. We're committed to a more equitable future and to helping our customers find new, sustainable ways of doing business. Join us and help create the critical insights that truly make a difference.
Our Values
Integrity, Discovery, Partnership
Benefits
- Health & Wellness: Health care coverage designed for the mind and body.
- Flexible Downtime: Generous time off helps keep you energized for your time on.
- Continuous Learning: Access a wealth of resources to grow your career and learn valuable new skills.
- Invest in Your Future: Secure your financial future through competitive pay, retirement planning, a continuing education program with a company‑matched student loan contribution, and financial wellness programs.
- Family Friendly Perks: It's not just about you. S&P Global has perks for your partners and little ones, too, with some best‑in‑class benefits for families.
- Beyond the Basics: From retail discounts to referral incentive awards—small perks can make a big difference.
Requirements
- 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
Responsibilities
- 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.
- 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.
- Ensure the team follows best practices for building robust, scalable, and maintainable ML solutions, including data pipelines, training workflows, retrieval systems, and model deployment.
- Oversee the development and evaluation of LLM-powered agents and grounded retrieval systems that use trusted S&P datasets to produce accurate, verifiable results.
- Collaborate closely with Product Management and cross-functional leaders to identify opportunities, define problem statements, and align ML initiatives with business objectives.
- Establish strong engineering practices, maintain high code quality, and foster a culture of reliability, observability, and continuous improvement across ML systems.
- Partner with Talent Acquisition to attract, interview, and onboard exceptional ML engineering talent as the ML organization grows.
- Contribute technically in design reviews, code reviews, modeling decisions, and architecture discussions, while empowering the team to own implementation and execution.
- Oversee monitoring, debugging, and performance evaluation of ML systems in production, ensuring reliability and consistent service quality.
- Work with Backend, Infrastructure, Product, and Data teams to ensure ML systems integrate seamlessly into Kensho's broader platform and applications.
Benefits
Skills
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