PitchBook Data
Machine Learning Engineer
Seattle, Washington, United States$125k–$180kmidAdded today
About this role
PitchBook seeks a Machine Learning Engineer to develop AI-powered features that extract insights from structured and unstructured financial data. You'll own end-to-end ML model development, from architecture through production deployment, focusing on NLP, generative AI, and LLMs to enhance the platform's discovery and intelligence capabilities.
What you'll do
- Design, build, and deploy production-grade ML models for NLP, semantic search, classification, and predictive tasks
- Own end-to-end ML lifecycle including architecture, training, optimization, and ongoing maintenance
- Extract insights from PitchBook's structured and unstructured data including reports, news, and textual content
- Collaborate with data scientists, engineers, and product managers to align technical solutions with business goals
- Develop scalable systems that meet production reliability and efficiency standards
- Contribute to technical excellence through code reviews, knowledge sharing, and architectural decisions
What they're looking for
- Machine learning model development and optimization
- Natural language processing (NLP) and large language models (LLMs)
- Generative AI and foundational model implementation
- Python and production-level software engineering
- Data pipeline and scalable systems design
- Model deployment and MLOps practices
- Semantic search and text classification
- Collaborative problem-solving and technical communication
Benefits
- Learning programs and mentorship opportunities
- Collaborative, innovative work environment
- Impact on financial industry through AI-powered insights
- Access to large, rich datasets for ML development
- Global team collaboration
- Investment in employee growth and development
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PitchBook Data
Likely interview questions
- Describe your experience building and deploying ML models in production. What were the biggest challenges you faced in operationalization?
- How have you approached NLP or LLM-based projects? What frameworks and tools did you use?