Staff Innovation Engineer, Marketplace

Responsibilities

• Design and build durable, reusable Marketplace services and primitives, including purchase orders, contract terms, invoicing rails, and workflow/status state machines.
• Use fine-grained microservices and asynchronous workflow orchestration such as Temporal.
• Partner directly with customers to scope problems and ship working versions of new marketplace products, then iterate based on customer feedback.
• Optimize launched products, workflows, click paths, quality, craftsmanship, adoption, and usage.
• Build and refine agent harnesses and layered prompting systems to automate workflows involving unstructured documents.
• Use AWS Textract, Google Cloud Document AI, Claude, Gemini, and OpenAI based on document needs and cost profiles.
• Build and use evaluation tooling to test and improve AI systems before and after production deployment.
• Partner with product, design, sales, and the General Manager on customer problems, pricing, go-to-market input, and product decisions.
• Mentor engineers through code review and pairing.
• Contribute to system design and architecture decisions.
• Maintain service and product API boundaries as the team scales.

Requirements

• 7+ years of software engineering experience.
• Strong backend and distributed-systems fundamentals, including designing reliable and maintainable services and APIs.
• Experience building and shipping 0-to-1 products or features in fast-iteration environments with real customers.
• Experience integrating multiple LLM providers, such as Claude, Gemini, and OpenAI.
• Experience with document-extraction tooling such as AWS Textract and Google Cloud Document AI.
• Experience building agent harnesses, RAG pipelines, or other AI/LLM-integrated workflows.
• Experience with evaluation or testing practices for AI/LLM systems.
• Comfort working across the stack in a full-stack-capable engineering role.
• Curiosity about customer problems, low ego, and a preference for close collaboration with product.

Nice-to-haves

• Data engineering, machine learning, or heavy document-processing experience.
• Experience in financial services, construction, real estate, payments, lending, insurance, or other complex B2B environments.

Compensation and benefits

• No compensation or benefits details were provided in the posting.