Senior Machine Learning Engineer

• Bachelor's degree or higher in Computer Science, Engineering, or a related field.

• 5+ years of significant, hands-on industry experience with machine learning, natural language processing (NLP), and information retrieval systems, including designing, shipping, and maintaining production systems.

• Strong proficiency in Python.

• Experience reading and understanding SQL databases and writing queries for specific access patterns. 

• Proven experience building ML pipelines for data processing, training, inference, maintenance, evaluation, versioning, and experimentation.

• 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.

• Experience working with machine learning libraries/frameworks for Large Language Model (LLM) orchestration, such as Langchain.

• (Preferred) Experience working with RAG based system 

What You’ll Do:

• Develop Advanced ML Systems: Create, refine, and deploy machine learning systems that solve complex business problems and power Kensho products.

• Build Retrieval-Driven AI Agents: Design AI agents that fetch, validate, and structure data from S&P datasets, ensuring answers produced by LLMs are grounded in S&P’s data universe.

• Evaluate LLM-based Agents: Identify and resolve performance gaps in both online and offline settings, addressing issues such as performance, latency, memory usage, compute efficiency, and feature consistency.

• Work With Domain Specific Data: Leverage proprietary structured and unstructured datasets, deep dive to have domain understanding, work with Subject Matter Experts (SMEs).

• Scale ML Applications: Optimize and scale ML systems to support high demand, efficient resource utilization, and reliable production behavior.

• Reduce Technical Debt: Proactively identify areas of the stack that can be improved, and propose solutions that strengthen reliability and maintainability.

• Taking Initiative: Scope, plan, and execute ML initiatives that develop core capabilities across Kensho products.

• Collaborate Across Teams: Work closely with Data, Product, Design, and Engineering teams to ensure smooth operations and contribute to long-term product vision.

• Improve User Experiences: Partner with Product and Design to develop ML-driven functionality that enhances user workflows and aligns with business needs.

• Drive the ML Lifecycle: Engage in all phases of the ML lifecycle, from problem framing and data exploration to model deployment and production monitoring, ensuring continuous improvement.

Technologies We Love:

• Traditional ML: Scikit-learn, 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