Machine Learning Engineer at pitchbookdata

Location: Seattle, Washington
Posted: Jul 10, 2026

Description

PitchBook, a Morningstar company, is seeking a Machine Learning Engineer (MLE) to join their AI & ML (Insights) team. The MLE will deliver AI-powered features that extract meaningful insights from structured and unstructured data, focusing on natural language processing (NLP), generative AI (GenAI), large language models (LLMs), and scalable data systems. This role involves end-to-end development and operationalization of ML models, collaborating with cross-functional teams to align with business goals and AI/ML strategy.

Required Skills

Data Analytics
Machine Learning
Natural Language Processing (NLP)
Generative AI (GenAI)
Large Language Models (LLMs)
Scalable Data Systems

Additional Information

Preferred Qualifications: Deep technical expertise in advanced data analytics and machine learning.
Hands-on approach to designing, building, and optimizing ML solutions.
End-to-end development and operationalization of ML models.
Architecture, training, deployment, and ongoing maintenance.
Focus spans across natural language processing (NLP), generative AI (GenAI), large language models (LLMs), and scalable data systems.
Tackle complex technical challenges.
Contribute to architectural decisions.
Collaborate closely with other engineers, data scientists, and product managers.
Technical excellence by sharing knowledge, pairing with teammates, and actively participating in code and design reviews.
Provide situational guidance to junior engineers and contribute to team best practices.
Build and optimize models that leverage classifiers, transformers, LLMs, and other NLP techniques.
Integrate these models into the broader AI/ML infrastructure in collaboration with partner teams.
Explore and experiment with emerging technologies, methodologies, and tools in the fields of GenAI, NLP, and search.
Translate research findings into practical solutions that enhance PitchBook’s AI capabilities.
Best practices in model transparency, monitoring, evaluation, and compliance.
Maintain high standards of security, data integrity, and responsible AI use across your projects.
Participate in the technical evaluation of candidates and help onboard new team members by contributing to documentation, pairing, and knowledge-sharing practices.
Apply principles from Agile, Lean, and Fast-Flow methodologies to support efficient model development and deployment cycles.
Support the vision and values of the company through role modeling and encouraging desired behaviors.
Participate in various company initiatives and projects as requested.