About SimplicityDX: SimplicityDX (SDX) converts creator user-generated content (UGC) into structured, verified, agent-readable social proof for AI-powered commerce. We operate as infrastructure for the agentic commerce era, not as a content or marketing tool. Our platform began by solving a real-time problem for the creator economy: as a creator’s post goes live, SDX ingests it, matches it against brand product catalogue and the creator’s own media, and assembles a purpose-built storefront on the fly. The creator gets a single link to drop into their social call-to-action, and that link resolves to a shoppable experience built for that exact post. We are now extending that same ingestion, verification, and assembly capability to a bigger problem: AI shopping agents (ChatGPT Shopping, Google AI Mode, Perplexity, Gemini, and the frameworks that will follow them) can already retrieve structured product data — price, specs, availability. What they cannot do reliably is judge whether a product actually works for a specific person or use case. SDX’s verified creator UGC is built to close that gap: human-validated evidence, creator context, and use-case-specific claims that an AI agent can reason over rather than merely retrieve. Our mission is to make verified, trustworthy UGC a standard, queryable layer of the agentic web — available not just on our own storefronts, but to any AI chatbot or agent that shops on a consumer’s behalf.
Why Lisbon: Lisbon is our first office outside our current base, and this hire is one of the founding members of that team. Early engineers here will have an outsized hand in how the office’s engineering culture, standards, and working practices take shape.
What You’ll Work On: SDX engineers work across a pipeline that runs from raw creator content to something an AI agent can trust and act on. Depending on the role, that includes: Real-time ingestion — pulling and normalizing creator content across heterogeneous, constantly- changing social platform APIs. Claim and evidence extraction — turning creator speech and video into structured, verifiable product claims, and linking each claim to the specific timestamped clip that supports it. Verification infrastructure — the pipelines and tooling behind our multi-layer human verification process, which produces the confidence signals that make our data trustworthy to both shoppers and AI agents. Storefront and evidence-page assembly — dynamically building shoppable storefronts and structured evidence documents (schema.org, custom SDX schema types) from a blend of brand catalogue and creator UGC. Distribution to AI surfaces — exposing verified UGC through commerce feeds (e.g. Google Merchant Center) and through direct agent-facing interfaces such as MCP (Model Context Protocol) endpoints, so AI shopping agents can query our data at inference time. This is genuine infrastructure work — real-time systems, structured data and schema design, integrations with commerce and social platforms, and building for consumption by AI agents rather than only human browsers — not content tooling or campaign management. How We Work: Embracing an AI-Native Engineering Culture: SDX is deliberately building an AI-native engineering culture, and we want that reflected in how the team works day to day, not just in what we build for customers. We expect engineers to work comfortably with an AI coding agent that is integrated directly into our codebase and issue tracker — using it to scaffold, implement, refactor, investigate, and triage as a normal part of the job, not as a novelty. We care most about judgment: knowing what to hand to an AI agent, what to keep for careful human review, and when a task looks safe for AI to handle but actually isn’t. That judgment call — made pragmatically, case by case — matters more to us than raw familiarity with any one tool. Because our product itself is about verifying and trusting AI-consumed content, we hold our own engineering practice to a similar standard: move fast with AI assistance, but be deliberate about where verification, testing, and human sign-off are non-negotiable. We don’t expect candidates to be AI researchers. We do expect genuine, hands-on comfort with agentic developer tooling and a track record of using it sensibly rather than either avoiding it or over-trusting it.
Baseline Expectations: Based in or willing to relocate to Lisbon, Portugal. Fluent in English (written and spoken), including the ability to express technical concepts clearly in English — our team, tooling, and documentation operate in English. Able and willing to travel internationally, including to the United States and other parts of Europe, as the role requires. Comfortable working with AI coding agents integrated into the codebase and issue tracker, and able to reason pragmatically about the human/AI split of a given task.
Specific Responsibilities: Design and build the real-time ingestion pipelines that pull and normalize creator content across heterogeneous, constantly-changing social platform APIs — handling scale, backpressure, and platform- specific quirks. Build and operate the verification infrastructure and tooling behind SDX’s multi-layer human verification process, producing the confidence signals that make our data trustworthy to shoppers and AI agents. Own distribution of verified UGC to AI-facing surfaces, including commerce feeds (e.g. Google Merchant Center) and direct agent-facing interfaces such as MCP (Model Context Protocol) endpoints. Design and evolve the schemas and data models (schema.org, custom SDX types) that make claims and evidence machine-readable and queryable at inference time. Instrument and monitor these systems for reliability, latency, and data quality, and take part in on-call and incident response for the backend/infrastructure surface area you own. Partner closely with the verification, storefront, and platform teams to keep a consistent contract between ingestion, verification, and downstream consumption. Work hands-on with an AI coding agent integrated into our codebase and issue tracker as a normal part of scaffolding, implementation, and triage — exercising judgment about what to delegate versus review carefully.
Required Skills & Experience: 5+ years of professional software engineering experience, with a track record of owning backend or infrastructure systems in production. Strong proficiency in Go (Golang) for building backend services; experience with Python is a plus. Experience designing and operating distributed, real-time or near-real-time systems (queues, streaming, event-driven architectures). Solid grounding in API design and modern microservice architecture, along with both SQL and NoSQL data stores. Track record of writing clean, well-tested, production-grade code that the rest of the team can build on. Hands-on experience with AWS and cloud infrastructure. Experience with Docker and Kubernetes for containerization and orchestration. Strong grasp of security best practices for backend systems and services handling user and creator data. Demonstrated ownership of system design at a senior level — leading design decisions and reviews, not just implementing them. Comfortable working with AI coding agents integrated into the codebase and issue tracker, with sound judgment about what to hand to AI versus keep for careful human review. Able to build out a team and mentor younger developers as the Lisbon office grows. Fluent in English (written and spoken), including the ability to express technical concepts clearly in English.
Nice-to-Have Skills: Experience building AI agents. Familiarity with e-commerce platforms, commerce feeds (e.g. Google Merchant Center), or agent-facing protocols such as MCP. Experience with NLP or claim/evidence extraction from unstructured text or video. Prior experience as an early engineer at a startup, or standing up engineering practices for a new office or team.
Compensation & Benefits: Base salary of €45,000–€55,000, plus stock options.
Reporting Line & Team: Reports to the Head of Engineering.
