Manager, Engineering, AI & ML

PitchBook PitchBook · Fintech · Seattle, WA · Product

Manager for an AI & ML Engineering team focused on generating insights from structured and unstructured data using NLP, GenAI, and LLMs. The role involves hands-on technical leadership, managing and mentoring engineers and data scientists, and overseeing the end-to-end lifecycle of AI/ML models and services, including summarization, semantic search, prediction, and classification.

What you'd actually do

  1. Drive the execution of AI & ML initiatives related to PitchBook Platform insights, ensuring that the team’s efforts are aligned with overall business goals and strategies
  2. Provide hands-on technical leadership in the engineering of AI/ML models and services, focusing on NLP, summarization, semantic search, prediction, classification, and other use cases. Oversee and contribute to the implementation of scalable solutions that meet high standards of reliability and efficiency
  3. Lead, mentor, and develop a high-performing team of engineers and data scientists, fostering a culture of innovation and continuous improvement. Ensure effective communication and coordination within your team and across geographically dispersed teams
  4. Contribute to the development and application of NLP techniques, including classifiers, transformers, LLMs, and other methodologies, to efficiently leverage structured and

Skills

Required

  • AI/ML initiatives
  • advanced structured and unstructured data analytics
  • managing and mentoring engineers
  • NLP
  • GenAI
  • LLMs
  • MLOps
  • data architecture
  • cloud-managed services
  • summarization
  • semantic search
  • prediction
  • classification
  • transformers

Nice to have

  • customer empathy
  • collaboration
  • diverse points of view
  • constructive discussions
  • integrity
  • growth
  • business value
  • learning
  • continuous improvement
  • innovation
  • teamwork
  • purpose

What the JD emphasized

  • deep technical expertise
  • hands-on approach
  • deep engagement
  • strong technical direction
  • problem-solve complex technical challenges
  • deep knowledge
  • end-to-end lifecycle

Other signals

  • managing engineers
  • AI/ML initiatives
  • generating insights
  • structured and unstructured data
  • NLP
  • GenAI
  • LLMs
  • MLOps
  • data architecture
  • cloud-managed services
  • end-to-end lifecycle
  • summarization
  • semantic search
  • prediction
  • classification