Machine Learning Engineer

About the Team

We are looking for a Senior ML Engineer to join the group of Machine Learning Engineers working on developing a cutting-edge GenAI platform, LLM-powered applications, and fundamental AI toolkit solutions such as . We are looking for talented people who share our passion for bringing robust, scalable, and highly accurate ML solutions to production.

You can read about some of our cutting-edge GenAI application at:

About The Role

As a Senior ML Engineer, you will work across a broad set of machine learning areas at Kensho. This includes building retrieval-driven AI agents that ground LLM outputs in trusted S&P data, as well as developing and operating production-grade machine learning systems that power our essential AI toolkits like Kensho or . Across these areas, your focus will be on creating reliable, scalable, and transparent ML solutions that deliver real value to users.

This role is ideal for engineers who:

• Enjoy building robust, production-grade ML systems end to end
• Have strong ML fundamentals, with a practical understanding of the backend and deployment work needed to run these systems in production
• Want to work across data, retrieval, modeling, and system design

You will partner with ML engineers, backend engineers, and product managers to drive the next generation of Kensho??s AI products.

We are seeking a mid-to-senior level Machine Learning Engineer who can help elevate and streamline our ML development lifecycle, setting a high bar for excellence in modeling, deployment, and production operations.

What We Are Looking for-

• 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), 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.