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, and that starts with the people behind the produ
What this role actually needs.
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, and that starts with the people behind the produ Responsibilities: - 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 (e.g., time or cost savings, improved quality or compliance). - 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. Requirements: - Software engineering foundation. A CS, Engineering, Data Science, or related degree (or equivalent experience), with demonstrated ability to build and operate production systems — backend services, internal tools, integrations, or data applications. - 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. You can reason about when to use these patterns and when simpler approaches suffice. - System design and architecture thinking. You can sketch a data flow, reason about integration points, evaluate trade-offs between approaches, and design for failure — including fallbacks, retry logic, timeouts, and human escalation paths. - Data and security judgment. You understand data access controls, the risks of giving AI broad access to sensitive information, PII minimization, audit logging, and what responsible data handling looks like in an enterprise environment. You know to filter data before it reaches the model, not after. - Business function acumen. You can engage credibly with stakeholders in Marketing, Finance, Sales, HR, Legal, or Operations — understanding their workflows, KPIs, and constraints well enough to scope solutions that fit their real needs. Company context: Pinterest is the visual discovery platform that powers idea search and shopping across web and mobile.
Day-to-day expectations
Pinterest lists these responsibilities for the AI Solutions Engineer role.
- 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 (e.g., time or cost savings, improved quality
- 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.
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- Software engineering foundation. A CS, Engineering, Data Science, or related degree (or equivalent experience), with demonstrated ability to build and operate production systems — backend services, internal tools, integrations, or data applications.
- 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. You can reason about when to use these patterns and when simpler approaches suffice.
- System design and architecture thinking. You can sketch a data flow, reason about integration points, evaluate trade-offs between approaches, and design for failure — including fallbacks, retry logic, timeouts, and human escalation paths.
- Data and security judgment. You understand data access controls, the risks of giving AI broad access to sensitive information, PII minimization, audit logging, and what responsible data handling looks like in an enterprise environment. You know to filter data before it reaches the model, not after.
- Business function acumen. You can engage credibly with stakeholders in Marketing, Finance, Sales, HR, Legal, or Operations — understanding their workflows, KPIs, and constraints well enough to scope solutions that fit their real needs.
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Open the source listing from pinterestcareers.com, confirm the role is still active, then apply on the employer or ATS page.
Source: pinterestcareers.com · Source ID: 7714127 · Confidence: 90/100 · Last checked: May 7, 2026
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