AI Engineer

Responsibilities:

• Deal sourcing: systems that discover companies, founders, and emerging signals before they become obvious. Think agents that search, enrich, connect, and rank fragmented information with a human investor firmly in the loop. Talent Radar: a living map of exceptional builders. Help us follow technical talent, founder trajectories, team formation, and the networks where the next company may emerge.
• Market intelligence: tools that turn messy public and private signals into evolving market maps, company landscapes, theses, and questions worth pursuing.
• The firm's memory: retrieval, research, and knowledge systems that make every conversation, memo, and decision more useful the next time around.
• Precise rails: the less glamorous foundations that make everything else trustworthy; data pipelines, identity resolution, permissions, evaluation, observability, feedback loops, and reliable cloud infrastructure. Some of these products will look like agents. Some will look like copilots, workflows, or quiet infrastructure. You will choose the form that best solves the problem, with a bias toward AI-native solutions over traditional software.
• Gather requirements from all the team members and make a product roadmap
• Prototype in days, put work in front of real users, and learn from usage.
• Build evals and feedback loops alongside the product, not after it.
• Use human judgement where it creates an edge; automate the rest.
• Make speed and precision allies. A sourcing system that is fast but noisy is not useful.
• Move comfortably from model behavior and prompt design to APIs, data, and production infrastructure.
• Your toolkit: We care more about what you can build than a checklist of credentials.

Requirements:

• You will likely be strong in Python and modern AI application development.
• Harness engineering: giving models the context, tools, memory, evaluation, and guardrails they need to do dependable work.
• LLMs, retrieval, agents, structured and unstructured data, and the judgment to know when not to use any of them.
• Cloud systems and the production basics needed to make prototypes reliable, secure, and observable.
• Product sense: turning an ambiguous operating problem into a simple experience people actually use.
• Clear communication with investors, founders, and technical teams.