Manager, Software Engineering - Design Systems Adherence

Role Overview

As the Engineering Manager for Design Systems Adherence, you'll lead the team building the workflows, tools, and quality feedback loops that help customers create design and code that correctly adheres to their design system when working with Figma’s Agents. This work spans context engineering (making guidelines, examples, and documentation useful to AI), agent architecture improvements, and creating datasets, evaluators, and benchmarks that measure how well outputs follow a customer's design system. You'll set strategy across these areas, raise the bar on adherence measurement, and turn evaluation results and customer feedback into feedback loops that improve design quality.

This role can be held from one of Figma's US hubs or remotely in the United States.

What you’ll do at Figma

• Lead and grow a 6-8 engineer team responsible for Design Systems Adherence across our workflows
• Coach and develop engineers through career growth, feedback, and technical leadership, building a culture of ownership and high-quality execution
• Own and evolve design system context: make guidelines, examples, documentation, and other guidance available to AI as useful, actionable context that improves how it applies a customer's system
• Set the technical strategy for adherence measurement, including high-fidelity evaluation datasets, quality rubrics, evaluators, and deterministic regression checks that catch issues as Figma's Agent evolves
• Build benchmarking, tools, and dashboards that help Figma and customers understand design system adherence and act on the results, while tracking visual quality, latency, and cost
• Partner closely with Product, Design, and AI Research teams at Figma to achieve business impact

Qualifications

Minimum Qualifications

• 3+ years of experience leading and growing high-impact engineering teams with a track record of strong impact
• Hands-on depth in building AI assistants, evaluation systems, and using modern large language models and harnesses
• Fluency with modern AI development and evaluation workflows, such as context management, dataset curation, automated evaluators, benchmarking, and regression testing
• Strong foundation in software engineering and a platform mindset: building tools and systems other teams adopt and rely on, alongside workflows that solve customer problems
• High quality bar for customer experience, data quality, and reliable measurement, with the ability to set technical direction and drive cross-functional alignment in complex environments

Preferred Qualifications

• Strong understanding of design systems, including components, tokens, usage guidelines, and their integration across design and code
• Proven ability to establish evaluation and customer feedback loops to continuously improve AI products
• Skilled in benchmarking AI products across output quality, latency, and cost, translating findings into actionable product priorities
• Track record of developing shared evaluation tools, curated datasets, or dashboards to measure quality and prevent regressions

Interview & Application Rules

• Candidates are required to keep their cameras on during video interviews.
• In-person onboarding attendance is required if hired.
• Candidates may submit up to 5 applications during any rolling 30-day period.
• Candidates may not reapply to the same role while an application is active or within 90 days of receiving a rejection.