Sr. Systems Engineer, Sales & Marketing

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 product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

At Pinterest, AI isn’t just a feature, it’s a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

Are you passionate about building impactful products for sales and finance teams? Come join the IT Enterprise Systems team at Pinterest where you will be responsible for advancing our sales and marketing systems.

• Design, build, and operate full‑stack applications and services on Pinterest’s enterprise infrastructure to support our Sales, Marketing, and Finance teams, from backend services and APIs through integrations and user‑facing workflows.
• Lead the technical design and implementation of GenAI/ML‑powered services and pipelines that automate and augment enterprise workflows (for example, summarizing sales interactions, enriching account data, or surfacing intelligent recommendations), including clear evaluation frameworks, observability, and validation guardrails.
• Own the end‑to‑end software development lifecycle for these systems: requirements gathering, architecture, implementation, testing, deployment, monitoring, and continuous improvement.
• Partner closely with product managers, business systems analysts, and other engineers to translate ambiguous business needs into well‑defined technical plans, success metrics, and deliverables—and to make thoughtful tradeoffs between scope, risk, and speed.
• Use AI coding tools and agents to accelerate development responsibly (e.g., drafting code and tests, iterating on designs, summarizing findings), while applying rigorous testing, code review, and observability to ensure quality and reliability.
• Mentor and coach other engineers on system design, GenAI/ML patterns, and AI‑augmented workflows, contributing to technical decision‑making and raising the bar on engineering practices within the team.

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field, or equivalent combination of education and experience.
• Experience building systems or analytics used by Sales and/or Marketing teams (for example CRM or sales productivity tools, campaign management and measurement, or revenue operations workflows), with a clear understanding of how these partners use data and tooling to hit their goals.
• 10+ years of software engineering experience building and operating production systems, including full‑stack applications and/or backend services on a modern cloud platform (such as AWS or similar). Experience in enterprise or business systems environments is a plus.
• Strong proficiency in Python and experience with at least one of:
  Java, Node.js, and/or Java script, with a track record of delivering reliable, maintainable code in collaborative engineering teams.
• Hands‑on experience designing and operating distributed systems and robust, fault‑tolerant integrations (e.g., REST/gRPC services, event‑driven architectures, or data pipelines) that support critical business workflows.
• Practical experience building or integrating GenAI/ML‑powered features or services in production—such as LLM‑based workflows, ML‑driven recommendations, or intelligent automation—including an understanding of evaluation,…