AI Solutions Engineer

About Pinterest

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we're on a mission to bring everyone the inspiration to create a life they love.

We're building a new capability at Pinterest: embedding AI-native engineering directly inside our business functions. The AI Solutions Engineer will partner with teams across Marketing, Finance, Sales, HR, Legal, and other functions to surface high-value automation opportunities, then design and ship the AI-powered tools that bring those opportunities to life.

This is a hands-on, mid-level software engineering role for someone who is equally comfortable reading a business process flowchart and writing production-grade Python. You'll work end-to-end — from discovery and scoping through prototyping, launch, and iteration — using the latest agentic frameworks, tool-calling patterns, and responsible AI practices.

What you'll do:

• Discover and scope AI opportunities: Partner with internal teams across corporate functions to understand their workflows, pain points, and goals, and identify high-value AI/automation opportunities. Map and improve business processes: document current workflows, identify bottlenecks, and propose AI-enabled changes that deliver clear business outcomes.
• Design end-to-end AI solutions: Design and implement AI-enabled tools and workflows that integrate with existing systems and data sources, and that are intuitive for non-technical users.
• Build and ship production-quality software: Write clean, maintainable code and tests. Use standard CI/CD and environment practices. Implement logging, monitoring, and basic guardrails so we can understand and improve performance, quality, cost, and reliability over time.
• Pilot, rollout, and drive adoption: Pilot, roll out, and drive adoption of solutions by working closely with end-users, gathering feedback, and iterating based on real-world usage.
• Champion for responsible AI: Ensure solutions follow privacy, security, and compliance expectations, especially when working with sensitive or regulated data.
• Build for reuse: Create and share reusable patterns, components, and documentation to make future AI/automation work faster and more consistent across teams.
• Accelerate Workflows with Generative AI and Automation: Leverage AI to accelerate execution, explore alternative solutions, synthesize information, automate repeatable work.

What we're looking for:

• Software engineering foundation. A CS, Engineering, Data Science, or related degree (or equivalent experience), with demonstrated ability to build and operate production systems.
• Hands-on AI and automation delivery. You've shipped AI-powered or automation-driven solutions in a real environment. Examples include: a multi-step workflow automation, an internal tool using document understanding or intelligent routing, or an integration of an AI service (e.g., OpenAI, Anthropic, Vertex AI, Bedrock) into an existing system.
• Agentic AI literacy. You understand how modern agentic systems are constructed — the difference between local and remote agents, how MCP (Model Context Protocol) works, what Agent Skills and Hooks are for, and how A2A (Agent-to-Agent) coordination is structured.
• System design and architecture thinking. You can sketch a data flow, reason about integration points, evaluate trade-offs between approaches, and design for failure.
• Data and security judgment. You understand data access controls, the risks of giving AI broad access to sensitive information, PII minimization, audit logging.
• Business function acumen. You can engage credibly with stakeholders in Marketing, Finance, Sales, HR, Legal, or Operations.
• Clear, collaborative communication.

Preferred Qualifications:

• Experience working embedded with or alongside corporate / G&A functions.
• Practical experience with agentic frameworks such as LangGraph, Claude Agent SDK, or comparable tooling.
• Familiarity with MCP server design — including building, deploying, and securing MCP-compliant tool servers.
• Experience designing and evaluating AI outputs at scale: eval sets, sampling pipelines, human-in-the-loop review queues, or A/B testing of AI-powered features.
• Exposure to responsible AI frameworks.
• Experience with RAG pipelines, vector databases, or enterprise search integrations.
• Familiarity with CI/CD for AI: prompt versioning, model version pinning, regression testing for LLM-powered features.

Salary: $123,696 — $254,667 USD (base salary; role also eligible for equity). US based applicants only.

In-Office Requirement: This role will need to be in the office for in-person collaboration 1-2 times every 6 months and therefore can be situated anywhere in the country.