Machine Learning Engineer

PitchBook PitchBook · Fintech · Seattle, WA · Product Development

Machine Learning Engineer on the AI & ML (Insights) team responsible for delivering AI-powered features that extract insights from structured and unstructured data. The role involves end-to-end development and operationalization of ML models, focusing on NLP, GenAI, and LLMs, including architecture, training, deployment, and maintenance. The goal is to enhance user-facing features on the PitchBook Platform by inferring meaning and enriching datasets with predictive and generative intelligence.

What you'd actually do

  1. Deliver high-impact AI and ML capabilities that drive insight generation on the PitchBook Platform. Ensure your work contributes to broader business goals and is aligned with the team's strategic priorities
  2. Provide hands-on expertise in designing, building, and deploying AI/ML models and services with a focus on NLP, summarization, semantic search, classification, and prediction. Contribute to the development of scalable, high-performance systems that meet production-grade reliability and efficiency standards
  3. Contribute to a culture of 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
  4. Build and optimize models that leverage classifiers, transformers, LLMs, and other NLP techniques to generate meaningful insights from structured and unstructured data. Integrate these models into the broader AI/ML infrastructure in collaboration with partner teams
  5. Collaborate with engineering, product management, and data collection teams to ensure models are informed by high-quality data and support strategic product goals

Skills

Required

  • software engineering
  • machine learning engineering
  • AI/ML applications in insight generation, summarization, semantic search, and prediction
  • natural language processing (NLP)
  • machine learning
  • classifiers
  • transformer models
  • large language models (LLMs)
  • scikit-learn
  • pandas
  • numpy
  • TensorFlow
  • PyTorch
  • delivering production-grade GenAI or LLM-based systems

Nice to have

  • advanced degrees

What the JD emphasized

  • deep technical expertise
  • hands-on approach
  • end-to-end development and operationalization
  • natural language processing (NLP), generative AI (GenAI), large language models (LLMs)
  • complex technical challenges
  • architectural decisions
  • production-grade GenAI or LLM-based systems

Other signals

  • delivering AI-powered features
  • end-to-end development and operationalization of ML models
  • natural language processing (NLP), generative AI (GenAI), large language models (LLMs)