Machine Learning Engineer II

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 speech recognition, entity linking, document extraction, automated database linking, text classification, natural language processing, and more.

The Vector Team at Kensho is focused on designing and deploying production-grade machine learning systems that power our next-generation retrieval-augmented generation (RAG) pipelines. We specialize in building robust retrieval systems, scalable embedding infrastructure, and tightly integrated LLM pipelines that leverage unstructured data sources.

Our mission is to make complex unstructured data easily discoverable and actionable by building intelligent, retrieval-driven systems that enhance enterprise search, question answering, deep research, report generation, and knowledge discovery experiences across S&P Global platforms.

We are seeking a mid-level Machine Learning Engineer to help develop and scale RAG systems across the company. This is a hands‑on, full-lifecycle ML role with a strong emphasis on retrieval models, LLM orchestration, and system‑level thinking.

Kensho states that the anticipated base salary range for the position is 150k – 190k. In addition, this role is eligible for an annual incentive bonus and equity plans. At Kensho, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case.

• Design and implement end-to-end RAG pipelines that integrate embedding models, vector databases, and data retrieval agents
• Build and optimize retrieval systems over large-scale proprietary datasets using advanced embedding techniques
• Develop LLM-based solutions that orchestrate retrieval, generation, and ranking to deliver high-quality, context-aware responses
• Investigate and solve challenges in vector search, chunking and indexing strategies, and GraphRAG
• Work closely with Product and Design teams to build ML-based solutions that enhance user experiences and meet business objectives
• Collaborate closely with the ML Operations team to create automated solutions for managing the entire ML systems lifecycle, from initial technical design to seamless implementation

• 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), information retrieval systems and large‑scale text processing, including designing, shipping, and maintaining production systems
• Strong programming skills in Python, with a working knowledge of data processing tools and ML frameworks such as PyTorch, Transformers, and Hugging Face
• Experience working with machine learning libraries/frameworks for Large Language Model (LLM) orchestration, such as Langchain, LLamaIndex, etc
• Proven experience building ML pipelines for data processing, training, inference, maintenance, evaluation, versioning, and experimentation
• Experience working with vector databases (e.g., PGVector, Open Search, Pinecone) and understanding of similarity search techniques and vector indexing algorithms
• 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

• ML: PyTorch, Transformers, Hugging Face, Lang Chain, Llama Index
• Tools/Toolkits: Label Box, Weights & Biases, Open Search, PGVector, LiteLLM
• 
  ** Techniques** : RAG, Prompt Engineering, Information Retrieval, Data Embedding
• Deployment: Airflow, Docker, Kubernetes, Jenkins, AWS

• Medical, Dental, and Vision insurance
• 100% company paid premiums
• Unlimited Paid Time Off
• 26 weeks of 100% paid Parental Leave (paternity and maternity)
• 401(k) plan with 6% employer matching
• Gener…