Data and AI Engineer, Salesforce Ventures

Salesforce•Boston, MA

About The Position

You will be an engineer embedded inside a top-tier venture capital team — helping to improve the internal systems, data infrastructure, and AI-powered workflows that make a lean investment team move fast and make better decisions. You will also apply your technical expertise to deal processes by contributing to technical diligence and data-driven research that informs investment decisions. This is not a traditional engineering role. You are joining as a technologist who builds useful systems, navigates a complex technical environment, and solves real problems for a fast-moving team — with the added dimension of learning the venture business from the inside and applying your technical judgment to how we evaluate companies and markets.

Requirements

Strong programming ability in Python and SQL. Tools like Claude Code have fundamentally changed how we write software, and we encourage our team to use them. But AI-driven coding places increased emphasis on engineering judgment. You are accountable for the architecture of what gets built, the quality of what gets shipped, and the maintainability of what other people depend on.
Experience building data products or internal tools. You understand how to build something that is useful to downstream users.
Comfortable with infrastructure. You've worked with cloud services (we're mostly on AWS) and IaC tools (Terraform). You can navigate working in a complex technical environment.
Experience with or strong interest in AI/LLM systems. We work with the technology we invest in.
Strong written communication. You can take messy technical and market research and synthesize it into a clear, concise communication. You can distill complex technical topics into meaningful investment insight.
Genuine curiosity about technology markets. You follow AI, security, developer tools, infrastructure, or frontier tech because you're interested. You have opinions about products and companies.

Nice To Haves

Experience with Salesforce CRM.
Familiarity with ETL/ELT tools (Fivetran, dbt).
Experience with Terraform for infrastructure management.
Background in venture capital, growth equity, or startup operations.
Contributions to open-source projects or a public portfolio of technical work.
CS, engineering, or data science degree — or equivalent self-taught depth.
Experience with SaaS metrics, software business models, and/or venture finance.

Responsibilities

Design and build internal tools and applications — deal tracking, portfolio analytics, sourcing workflows, pipeline management, and ad-hoc data requests across investment, finance, marketing, and portfolio development teams.
Build and improve data pipelines and reporting infrastructure powering essential firm activities like portfolio reviews, deployment analysis, fund performance measurement, and pipeline analytics.
Build and maintain custom integrations — MCP servers, Slack apps, and other connectors — that tie together the tools ecosystem and give AI systems deep access to internal data.
Help own Salesforce CRM data architecture: object model design, field logic, automation rules, validation, and data quality enforcement.
Ideate, build, and deploy AI-powered automations that eliminate manual work across the investment lifecycle: email processing and triage, automated company tagging, meeting brief generation, and portfolio onboarding workflows.
Build reusable AI skills, prompt pipelines, and project configurations tailored to investment workflows: sourcing, diligence, competitive analysis, and investment committee preparation.
Help conduct technical diligence on active deals — product architecture, API design, developer experience, infrastructure choices, open-source positioning, scalability, and technical moats.
Contribute to deal memos, IC question responses, and technical appendices — translating engineering assessment into investment language.
Design and lead periodic AI enablement sessions for the team — workflow demos, hands-on building time, and best practices.
Evaluate new AI/ML tools for internal usage and support data quality across all connected systems.