PitchBook currently has 10 active AI-related job listings. The majority of these roles, 70%, are focused on agents. Engineering is the dominant function with 7 roles, followed by Product with 3. The company is hiring for roles that involve model serving, LLM observability, and agent orchestration.
Currently tracking 3 active AI roles, up 107% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $140k–$175k (avg $158k).
PitchBook currently has 6 active AI-related roles in our index. The most common open titles are: Manager, Engineering, AI & ML, Principal Product Manager, Deal Sourcing, Product Manager, Technical Platform Engineering, Sr. Director of Engineering, Data Collections Technology, Sr. Machine Learning Engineer. Most positions are in Product and Engineering.
PitchBook's active AI hiring is concentrated in: agents (50%), application (17%), post-training (17%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
PitchBook is hiring AI talent in: United States (6 roles).
Job postings at PitchBook most frequently reference: llm observability, model serving, fine tuning, rag, vector db.
In the past 30 days, PitchBook has posted 5 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Manager, Engineering, AI & ML 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. | Post-trainAgent | 8 |
| Sr. Machine Learning Engineer Senior Machine Learning Engineer on the AI & ML (Insights) team responsible for delivering AI-powered features that extract meaningful insights from structured and unstructured data using NLP, GenAI, and LLMs. This role involves end-to-end development, operationalization, and deployment of ML models, with a focus on summarization, semantic search, classification, and prediction. | AgentPost-train | 8 |
| Machine Learning Engineer 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. | AgentPost-train | 8 |
| Sr. Machine Learning Engineer Senior Machine Learning Engineer on the AI & ML (Insights) team responsible for delivering AI-powered features that extract meaningful insights from structured and unstructured data using NLP, GenAI, and LLMs. This role involves end-to-end development, operationalization, and deployment of ML models, focusing on summarization, semantic search, classification, and prediction to enhance user-facing features on the PitchBook Platform. | AgentPost-train | 8 |
| Machine Learning Engineer 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. | AgentPost-train | 8 |
| Sr. Software Development Engineer, AI Enablement Senior Software Development Engineer focused on building internal AI-powered tools and platforms to enhance developer and general productivity across PitchBook. The role emphasizes reducing friction, accelerating delivery, and embedding AI into everyday workflows, acting as a force multiplier for engineering, product, and research teams. Key responsibilities include designing and implementing AI-enabled productivity tools, leading complex initiatives, and partnering with various teams to identify high-impact AI opportunities. The role requires strong software development experience, practical experience with Generative AI/LLMs, and a focus on integrating AI into production workflows. | Agent | 7 |
| Engineering Manager, AI Enablement Engineering Manager to lead the team building and operating core platforms for AI adoption, focusing on managed tool access, context management, agent creation/governance, and workflow orchestration. The role emphasizes enabling product teams, data scientists, and the workforce with scalable, safe, and usable AI foundations, acting as a force multiplier for experimentation and operationalization of AI capabilities. | AgentServe | 7 |
| Staff Software Development Engineer, AI Enablement Staff Software Development Engineer, AI Enablement at PitchBook. This role focuses on defining, building, and scaling AI-powered productivity capabilities and internal platforms to enhance developer productivity and organizational workflows. The engineer will act as a technical leader, identifying high-leverage AI opportunities, setting technical direction, and delivering reusable solutions. Responsibilities include architecting scalable platforms, identifying bottlenecks, leading ambiguous initiatives, and establishing best practices for AI usage, quality, and safety. The role requires strong Python skills, experience with LLMs/GenAI, prompt engineering, evaluation strategies, and responsible AI patterns. | Agent | 7 |
| Engineering Manager, Machine Learning Operations Engineering Manager for an MLOps team responsible for enabling ML teams by optimizing the ML Development Life Cycle. The role supports projects in GenAI, LLMs, NLP, Classification, and Regression, and is critical for driving AI innovation. | ServeData | 7 |
| Engineering Manager, Machine Learning Operations Engineering Manager for an MLOps team responsible for enabling ML teams by optimizing the ML Development Life Cycle. The role supports projects in GenAI, LLMs, NLP, Classification, and Regression, and is critical for driving AI innovation. | ServeData | 7 |