Machine Learning Engineer

Job Summary

As a Senior ML Engineer, you will work across a broad set of machine learning areas. This includes building retrieval-driven AI agents that ground LLM outputs in trusted SP data, as well as developing and operating production-grade machine learning systems that power our essential AI toolkits.

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

Ideal for

• 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

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 SP datasets, ensuring answers produced by LLMs are grounded in SP'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