Principal Software Engineer

Pearson•United States,

10d•$150,000 - $170,000

About The Position

Pearson is modernizing its content platform into shared platform services. The Principal Software Engineer will lead this work hands-on, designing target systems and safely moving content and users onto them. The role spans user interfaces, workflow orchestration, AI, backend services, and cloud infrastructure. The engineer remains deeply involved in design and implementation while strengthening the technical judgment and execution of other engineers. This is a cross-platform, hands-on engineering role rather than an architecture-only position or a role embedded in one team. The engineer will lead initiatives and solve technical problems important to leadership, owning designs, critical-path code, and consequential engineering decisions. The role also includes setting long-term technical direction and serving as a subject-matter expert for the area. The engineer will partner with engineering managers, product managers, architects, quality engineering, platform users, stakeholders, internal teams, and external partners. They will assess and develop AI capabilities, evaluate tools and vendors, pilot engineering practices, and help teams adopt AI coding tools effectively. Success includes mentoring engineers and enabling senior engineers to become owners of the systems.

Requirements

12 or more years building and operating production software, including 4 or more years at staff or principal level, or as a technical lead across several teams or a large multi-service system.
Led a production-system re-platforming or legacy migration involving active users and data through the shutdown of the old system.
Led the design and delivery of technically complex, user-facing systems.
Upgraded or migrated production systems away from an end-of-life runtime or framework.
Full-stack depth in Java and Spring Boot, Node.js and TypeScript, and React.
Experience with MongoDB and event-driven services such as SQS, SNS, or Kafka, behind APIs with many consumers.
Production experience on AWS, particularly ECS Fargate, Lambda, S3, and API Gateway.
Experience with observability tools such as New Relic and CloudWatch, CI/CD with GitHub Actions, and infrastructure as code with Terraform.
Can write clear design documents and migration plans, explain tradeoffs to engineering, product, and executive audiences, and influence without authority, particularly amid uncertainty.
Bachelor’s degree in Computer Science or an equivalent combination of education and experience.

Nice To Haves

Experience with content platforms, including authoring and content management, digital asset management, content transformation pipelines, content delivery, or assessment systems.
Experience with content and learning standards such as EPUB, XML/XSLT, QTI, or LTI.
Built or operated a production LLM-backed feature using Python and services such as AWS Bedrock or Azure OpenAI, with understanding of AI challenges including nondeterminism, evaluation, latency, and safety.
Moved a product from a commercial platform, such as a DAM or CMS, to in-house services.
Experience with search and caching using OpenSearch or Solr and Redis.
Experience working with distributed teams and delivery partners across US and India time zones.

Responsibilities

Own technical plans and delivery for multiple technology tracks, including consolidations, migrations, and large-scale initiatives from target design through launch.
Lead migrations of large volumes of content and data, verifying that downstream products are not adversely affected.
Design shared platform capabilities such as media handling, search, rendering, and AI content services, and expose them through clean APIs.
Assess technical debt, estimate its cost, and prioritize remediation.
Serve as the technical point of contact for escalated incidents with business impact, following through on root cause and preventive fixes.
Maintain security and stability through runtime and framework upgrades, vulnerability and penetration test remediation, secure data handling, and documented, exercised, audit-ready disaster recovery plans.
Establish shared standards across teams for design patterns, tooling, reliability, observability, performance, and evaluation, including safe measurement and testing of AI-powered workflows.
Own the long-term technical roadmap and subject-matter expertise for the assigned area.
Write production code on high-risk work and review engineers’ most challenging changes.
Unblock difficult technical problems spanning systems, teams, or domains.
Work with engineering managers, product managers, architects, and quality engineering to make tradeoffs between long-term initiatives and current delivery visible and help decide them.
Work directly with platform users across Pearson’s lines of business to distinguish foundational problems from incremental ones before teams commit to work.
Identify opportunities spanning team and product boundaries, align stakeholders, and carry work from exploration through launch and iteration.
Make high-consequence technical decisions amid ambiguity, balancing speed and learning with scalability, simplicity, and maintainability, and adjust decisions when evidence changes.
Evaluate frameworks, tools, and vendors, and pilot practices such as contract testing, automated regression, and zero-downtime migration.
Run technical feasibility reviews for new AI capabilities and collaborate with internal teams and external partners to assess and co-develop them.
Use AI coding tools daily, particularly for large upgrades and refactors, and help teams adopt them with sound review practices.
Review critical designs, mentor engineers, and improve engineering practices so senior engineers can also own the systems.