Senior Software Engineer

Pearson•United States,

5d•$100,000 - $169,000

About The Position

Innovation Squad - Engineering Our charter is simple to state and ambitious to deliver: build what Pearson does next. We are a small, newly formed team within Pearson's Enterprise Learning & Skills (ELS) division, creating entirely new products for our enterprise clients rather than improving existing ones. Our starting point is an asset few companies can claim. Pearson has nearly two centuries of history and, with it, one of the world's deepest bodies of learning content, assessment data, and credentialing expertise. AI has transformed what that heritage makes possible. Our mission is to bring it to bear in new and distinctive ways - solving problems for our customers that could not be solved before AI. That charter is intentionally broad. Each product may address a different challenge for the organizations we serve, but everything we build shares three characteristics: It will always involve integrations. Our products operate inside our clients' ecosystems - learning platforms, HR systems, identity providers, and data pipelines - and must work seamlessly with all of them. It will almost always build on other Pearson products. Pearson's portfolio spans assessment, qualifications, credentials, learning content, and workforce skills. We combine these capabilities - and the data and insight behind them - in ways that were not possible before, collaborating across the company as much as within the team. It will be researched and validated before it scales. We take an evidence-led approach: concepts are tested with real customers, and only those that prove their value move forward. Some ideas will be retired and others will advance - both outcomes are signs of a disciplined process. The team is built around senior individual contributors - design engineers, product engineers, and a zero-to-one product manager - who work as peers from initial concept to shipped product.

Requirements

7+ years of software engineering with genuine full-stack range, and a track record of building products from scratch — as a founder, founding engineer, or member of a labs, incubation, or new-products team.
Integration depth: designing and consuming APIs (REST, GraphQL), auth and identity flows, eventing, and moving data between systems that were never designed to talk to each other.
Systems thinking: you can hold a complex enterprise environment in your head, find the load-bearing constraints, and design something that works within them.
Pragmatic engineering judgment: you optimize for learning speed early and reliability later, and you can explain exactly where and why you drew that line.
Comfort with modern AI-assisted development, and informed opinions about when AI belongs in a product and when it doesn't.
Strong communication with technical and non-technical audiences — including client-facing conversations where you're the engineering voice in the room.

Nice To Haves

Direct experience with learning technology standards and platforms: LTI, SCORM, xAPI, QTI, Caliper, the 1EdTech ecosystem, and LMS/LXP integration work.
Web standards depth (W3C, WHATWG) and practical accessibility knowledge (WCAG) — accessibility is a floor for everything we ship.
Standards-body, committee, or working-group participation.
Experience turning research — user or technical — into build decisions.

Responsibilities

Take new product concepts from architecture sketch to working software — prototypes, pilots, and MVPs — across the full stack.
Own integrations end to end: composing other Pearson products' APIs and platforms, and connecting into client-side systems — auth and identity (OAuth 2.0, OIDC, SAML), provisioning (SCIM), events and webhooks, and learning standards (LTI, SCORM, xAPI) where they apply.
Right-size the engineering to the stage of the bet: gloriously disposable code for a one-week concept test, production-grade foundations for products that graduate. Knowing which is which is the core skill.
Work directly with client technical teams during pilots — discovery, scoping, deployment, and debugging inside their environments.
Instrument everything for learning: telemetry, evaluation, and the data the team needs to make honest kill-or-scale decisions.
Use AI as a force multiplier — in your own development workflow, and, where a bet calls for it, as a capability inside the products themselves.
Contribute well beyond the code: shaping concepts, joining research sessions, and pressure-testing feasibility before anyone falls in love with an idea.
Partner with Pearson's platform, security, and architecture teams so that graduating products land well inside the wider organization.