Staff Software Engineer: AI-Driven, End-to-End Solutions

Pearson

Annapolis (MD)

On-site

USD 150,000 - 180,000

Full time

4 hours ago

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Job summary

Pearson is seeking a forward deployed engineer to sit with customers and deliver end-to-end software that solves real problems. You will operate autonomously, ship production AI applications, and drive adoption of AI across Pearson’s strategic lines of business.

The role requires 8+ years of full-stack experience, strong CS fundamentals, and deep expertise in React/TypeScript, backend and cloud deployments.

Qualifications

8+ years of full-stack engineering experience, with meaningful time spent shipping production systems end to end.
Strong CS fundamentals. Data structures, algorithms, system design. You can whiteboard a service architecture, talk through tradeoffs, and not flinch at a coding interview.
Frontend depth. Modern React, TypeScript, real component architecture, state management you can defend, and the taste to build something that looks finished — not just functional.
Backend depth in order of importance (Node.JS, Java, and Python). API design, data modeling, auth, error handling.
Production LLM experience. Advanced prompt engineering, agent development, evaluation frameworks, retrieval, and deployment at scale.
Fluency with AI-assisted development tools and agentic coding.
Data fluency. Databases at a real working level, comfort with Python data tooling.
Cloud and deployment fluency. AWS, GCP, or Azure — enough to put a service on a real URL behind real auth without filing a ticket.
Integration experience. You've built things that talk to systems you didn't write. SSO, OAuth, third-party APIs, internal platforms, legacy databases.
High agency. You navigate ambiguity inside a complex organization without needing someone to clear your path.
High cooperation, low ego. Pearson is 30,000+ people. Forward deployed work crosses team lines constantly.
Communication skills that work in both directions. You can sit in a room of executives and engineers, hold both threads at once, and not lose either audience.
Currency on what's changing. You stay close to the frontier of LLM capabilities, agent patterns, and AI product stacks.
Education: Bachelor's degree in Computer Science or equivalent combination of education, training, and professional experience

Responsibilities

Go where the problem is by working directly with internal stakeholders and architect end-to-end systems that users can actually run in production.
Conduct real discovery by sitting with users, observing workflows, and translating findings into buildable software.
Build during the conversation by delivering working software early and iterating rapidly.
Handle messy inputs (spreadsheets, PDFs, emails) and transform them into coherent, usable data pipelines.
Ship production AI applications on frontier LLMs, agents, MCP servers, and retrieval systems with measurable impact.
Iterate across multiple engagements quickly, adapting to changing requirements and feedback.
Own the full stack from frontend to observability and deployment, delivering a complete, production-ready service.
Codify patterns that work across engagements and feed improvements back to platform teams.
Maintain ongoing customer relationships across the lifecycle to identify opportunities and harden successful prototypes into production.