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# Machine Learning Engineer II

- **Company:** Sp Global, Inc.
- **Location:** New York, NY, United States
- **Experience:** Experienced
- **Salary:** $140,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Automated Storage and Retrieval Systems, Encodings, Computer Programming, Data Retrieval, Text Processing, Github, Information Retrieval, Python (Programming Language), PostgreSQL, Machine Learning, Natural Language Processing, Tensorflow, Unstructured Data, Management of Software Versions, Enterprise Search, Pinecone, Data Processing, Pytorch, LangChain, Retrieval-Augmented Generation, Large Language Models, Claude Code, Database Optimization, Prompt Engineering, Llamaindex, Agentic-AI, Question Answering, Pgvector, Kubernetes, Information Technology, HuggingFace, LiteLLM, Machine Learning Operations, Virtual Agents, Graph RAG, OpenSearch, Software Library, Docker, Jenkins
- **Published:** October 27, 2026
- **Apply:** https://www.juju.com/job/1_spglobal-330645

## About the Role

systems lifecycle, from initial technical design to seamless implementation **Who You'll Need:** + 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 HuggingFace + 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., PostgreSQL/PGVector, OpenSearch, 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 **Technologies We Love:** + **ML:** PyTorch, Transformers, HuggingFace, LangChain + **Tools/Toolkits:** Claude Code, Weights & Biases, OpenSearch, PostgreSQL/PGVector, LiteLLM + **Techniques** **:** Agentic Search, Prompt Engineering, Information Retrieval, Data Embedding, AI agent evaluation + **Deployment:** Airflow, Docker, Kubernetes, Jenkins, AWS, Github Action At Kensho, we pride ourselves on providing top-of-market benefits, including: + 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

## Description

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 140k - 180k.
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. **What You'll Do:** + Design and implement end-to-end RAG pipelines that integrate proprietary chunking algorithms, 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, unstructured data retrieval evaluation, 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