Staff Software Engineer

Pearson•Hoboken, NJ

12d

About The Position

Pearson's PSG-CP (Pearson Software Group - Content Platform) team is looking for a forward deployed engineer who shows up where the work is happening, listens to a problem that nobody has fully written down yet, and leaves with software that addresses it. Not a deck, not a spec, not a Jira epic - running software. You sit closer to the customer than a typical engineer and closer to the code than a typical solutions architect. The job is to compress the distance between "we need something that does X" and "here, try this." You will embed directly with our most strategic internal lines of business to drive AI adoption and ship working software against real problems. You will operate autonomously, thrive under ambiguity, and represent PSG-CP at the highest level inside the business. This is a significant responsibility — you'll play a key role in how AI shows up across Pearson.

Requirements

8+ years of full-stack engineering experience, with significant experience shipping production systems end to end.
Background as a technical founder, FDE, or software engineer with consulting experience.
Strong CS fundamentals including data structures, algorithms, and system design.
Frontend depth in modern React, TypeScript, component architecture, and state management.
Backend depth in Node.JS, Java, and Python, including API design, data modeling, auth, and error handling.
Production LLM experience, including advanced prompt engineering, agent development, evaluation frameworks, retrieval, and deployment at scale.
Fluency with AI-assisted development tools and agentic coding, with a demonstrable process for delivering measurable results.
Data fluency, including databases and Python data tooling.
Cloud and deployment fluency (AWS, GCP, or Azure), including CI/CD, containers, and observability.
Integration experience with systems like SSO, OAuth, third-party APIs, internal platforms, and legacy databases.
High agency: ability to navigate ambiguity and make independent decisions.
High cooperation and low ego.
Communication skills effective with both executives and engineers, and the ability to translate end-user problems into technical requirements.
Bachelor's degree in Computer Science or equivalent combination of education, training, and professional experience.

Nice To Haves

Experience as a technical founder, FDE, or software engineer with consulting experience.
Ability to whiteboard a service architecture, discuss tradeoffs, and perform well in coding interviews.
Taste to build something that looks finished, not just functional.
Comfort owning a service from request handler to schema.
Shipped at least one real LLM-backed application that someone other than you used.
Enough cloud and deployment knowledge to put a service on a real URL behind real auth without filing a ticket.
Experience building things that talk to systems you didn't write.

Responsibilities

Work directly with internal stakeholders inside different lines of business to understand pain points, workflows, and current technical landscape, then design and build systems end to end.
Conduct real discovery by sitting with users, observing their work, asking questions, and translating insights into buildable solutions.
Build initial versions of software rapidly, often within the same meeting as discovery.
Process and synthesize messy inputs such as spreadsheets, PDFs, screenshots, recorded calls, and documents from multiple stakeholders into coherent solutions.
Ship production AI applications using frontier LLMs, agents, MCP servers, evaluation harnesses, retrieval systems, and custom skills.
Iterate quickly across multiple engagements, adapting to shifting requirements.
Own the full stack, including frontend, backend, data, integrations, deployment, and observability.
Codify successful patterns (e.g., prompt structures, evaluation harnesses, agent templates) and contribute them back to the platform for reuse by other engineers.
Own the relationship with lines of business over time, identifying new opportunities and ensuring production-ready solutions.
Stay close to the frontier of LLM capabilities, agent patterns, and AI product stacks.