Lead Design Engineer

R/GA Careers Page

5h

About The Position

AIP is R/GA’s global group for developing its own products. Its first product, BRDNA Sequencer™, encodes a brand’s design system—including components, patterns, tokens, rules, and voice—into structured context that coding agents can use to create on-brand designs. The product translates Figma designs into markdown and back, enabling collaboration between designers or marketers and AI agents.

The Lead Design Engineer turns design systems into inputs AI can reliably build with. The role focuses on improving the two-way Figma-to-markdown encoding pipeline, building a self-serve onboarding experience, and designing and shipping the product’s customer-facing front end alongside full-stack product engineers. The team is still developing product-market fit and repeatable onboarding, so the role helps shape the product rather than simply maintain a finished system.

The position requires experience building and maintaining design systems, strong product design or front-end engineering skills, and practical expertise with prompt systems and model behavior. The successful candidate will iteratively diagnose output variance across source systems, data structure, retrieval, and prompts; improve onboarding using collaboratively defined self-serve KPIs; and build for dependable results across multiple AI models and platforms.

The product surface uses React and Next.js, with Supabase for data and backend services and Figma MCP to access design systems. The workflow also uses Cursor and Claude Code. The product is model-agnostic and is intended to work with Figma, Claude, ChatGPT, and Frontify. In the first 90 days, onboarding should be robust enough for teams to get started themselves, and workflows should produce more dependable results with reduced variance.

Requirements

• At least 5 years of experience in design engineering, product design, or front-end product work.
• Have built, owned, shipped, and maintained a design system, including making component and token-architecture decisions and adapting the system without breaking downstream users.
• Have designed a full product, including decisions about what to expose, extensibility, and adoption by subsequent teams.
• Build layered prompt systems that produce high-fidelity, production-grade assets; have shipped work held to a real quality standard rather than limited to demos.
• Understand how prompt structure affects model behavior and have a method for locating which layer is responsible when output quality degrades.
• Take ownership of shipped products and make decisions with long-term product health, technical debt, and future metrics in mind.
• Work effectively with model-agnostic systems and develop prompt work that holds up across models rather than relying on a single provider’s behavior.

Nice To Haves

• Experience with Figma as a platform, including Variables, the Plugin or REST API, Code Connect, or library architecture used by multiple teams.
• Practical experience with retrieval and embeddings, including chunking strategy, retrieval-augmented generation, fine-tuning, or LoRAs.
• Experience building evaluation systems or harnesses to measure model output rather than relying only on visual inspection.
• Experience shipping self-serve onboarding and improving activation metrics through product changes.
• Experience taking a product from 0 to 1 to 10 and maintaining it as customers begin using it.

Responsibilities

• Improve the design-system encoding pipeline, translating Figma components, patterns, tokens, rules, and voice into structured markdown and tokenized context for agent retrieval.
• Develop the reverse pipeline that translates structured understanding back into Figma designs that are faithful to the source system.
• Test agent outputs and diagnose whether output variance originates in the source system, structure, retrieval, or prompts; iterate to improve consistency across brands and models.
• Own and improve self-serve onboarding, moving teams from having a Figma library to using agents to create on-brand assets.
• Audit the existing onboarding experience, identify where users are lost, and rebuild the flow.
• Set self-serve KPIs with leadership and research partners and use results to guide product decisions.
• Design and ship the product’s customer-facing front end, building production software alongside full-stack product engineers.
• Contribute to product decisions and long-term product health, including extensibility, technical debt, and the needs of future adopting teams.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed