Staff Software Engineer - Frontline AI & Production

Pearson; Pierre (SD); On-site; USD 150,000 - 180,000; Full time

Job summary

Pearson's PSG-CP team is seeking a forward deployed engineer who sits where the work happens and ships software that solves real problems. You’ll work with stakeholders directly, build end-to-end systems, and deliver production AI applications using frontier LLMs and agent tech.

You will own the full stack, iterate across engagements, and maintain long-term relationships with lines of business while staying close to AI product stacks and deployment best practices.

Qualifications

• 8+ years of full-stack engineering experience shipping production systems end to end.
• Strong CS fundamentals in data structures, algorithms, and system design.
• Frontend depth with modern React and TypeScript.
• Backend depth: Node.js, Java, Python; API design and auth.

Responsibilities

• Go where the problem is: work with internal stakeholders and design end-to-end systems for real users.
• Conduct real discovery: observe users, ask questions, translate needs into buildable work.
• Ship production AI applications on frontier LLMs, agents, and related infra.
• Own the full stack: frontend, backend, data, deployments, observability.
• Iterate rapidly across engagements, shipping versions and improvements.
• Codify patterns and share improvements with platform/engineering teams.
• Maintain ongoing relationships with lines of business throughout engagements.

Skills

Full-stack engineering

React

TypeScript

Node.js

Java

Python

API design

OAuth/SAML

Cloud platforms

LLM applications

Data tooling

Education

Bachelor's degree in Computer Science

Tools

Swagger

Job description

Pearson's PSG-CP team is seeking a forward deployed engineer who sits where the work happens and ships software that solves real problems. You’ll work with stakeholders directly, build end-to-end systems, and deliver production AI applications using frontier LLMs and agent tech.

You will own the full stack, iterate across engagements, and maintain long-term relationships with lines of business while staying close to AI product stacks and deployment best practices.