{"id":4151954,"url":"https://alion.io/job/pearson-principal-software-engineer-4","title":"Principal Software Engineer","company":{"id":34029,"name":"Pearson","domain":"pearson.com","url":"https://alion.io/company/pearson","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"B","score":81,"open_postings":25,"ghost_share":0,"stale_share":0.72,"repost_share":0.04,"time_to_fill_p50_days":29,"computed_at":"2026-11-12T10:38:37Z"}},"role":"Backend","role_family":"Backend","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":155000,"max":175000,"currency":"USD","period":"year","gross":null,"usd_annual":175000},"salary_estimate":null,"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"LLM","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Multimodal AI","optional":false},{"name":"AI Agents","optional":true},{"name":"AWS","optional":true},{"name":"Function Calling","optional":true}],"status":"live","first_seen_at":"2026-11-12T08:32:11Z","employer_posted_date":"2026-11-12","last_verified_at":"2026-11-12T09:35:34Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Role Overview\nPearson Virtual Schools is transforming how learners experience education online. As our technology and programs evolve, so does how we design, build, and deliver them.\nIndividuals in the Software Engineer roles design, develop, test, and maintain software applications aligned with the company’s technical and functional architecture. They translate complex business requirements into scalable technical solutions, grounded in a deep understanding of the business, its drivers, and industry trends. They are responsible for software quality through thorough testing and lifecycle management. They collaborate with cross-functional teams to align on strategic objectives and deliver value through continuous improvement.\nPrincipal Software Engineers are relied upon to independently lead the delivery of technically challenging solutions within major product lines or back-end systems that support business revenue, regulatory needs, or operational continuity. At this tier, individuals solve intricate problems, advise on architecture choices for high-profile features, and frequently act as technical point of contact during escalated incidents with measurable business impact. They coordinate evaluation of new frameworks and tools, pilot advanced coding practices, and review the work of less experienced engineers for critical projects. The engineer’s influence regularly extends to cross-team collaborations, with their contributions making the difference in project timelines, technical debt, and system performance. Risk exposure involves safeguarding the stability and security of mid- to large-size applications and averting delivery or operational failures.\nThis role sits where three Pearson Virtual Schools programs meet: Content Foundations, Data Foundations and Adaptive Learning. You will own the technical architecture that connects AI-ready learning content and trusted learner data to adaptive, AI-powered learning experiences for K-12 students.\nKey Responsibilities\nCross-Program Technical Leadership\nOwn the end-to-end technical design where three programs meet: structured learning content, learner and performance data, and adaptive learning experiences\nDefine the shared contracts between content, data and adaptive services so each program can move independently without breaking the others\nAdvise on architecture choices for high-profile adaptive features, and make the tradeoffs between speed, quality and long-term platform health explicit\nAct as the technical point of contact for escalations that span more than one program, and drive them to resolution\nAI-Powered Adaptive Learning\nDesign and build LLM- and agent-driven capabilities that personalize the learning path based on what a student knows, where they struggle and how they learn\nEstablish evaluation suites and quality gates for AI-generated content and recommendations, covering accuracy, pedagogy, safety and bias\nLead the evaluation of new models, frameworks and tools, and pilot advanced practices such as retrieval, tool-using agents and MCP servers before scaling them\nPartner with curriculum and learning design experts so that adaptive behavior reflects sound learning science, not just technical possibility\nExecution & Delivery\nBuild working prototypes quickly to test ideas with real learners and educators, then harden the ones that prove out into production-grade services\n\nReduce systemic risk through refactoring, hardening and observability across the content, data and adaptive services you touch\n\nSafeguard the security and privacy of student data in every design, meeting the standards expected of a K-12 education provider\n\nMentorship & Influence\n\nModel AI-first engineering, using agents throughout design, build, test and review, and help teams across the three programs do the same\n\nReview critical designs and code from engineers across teams, and raise the bar through example rather than mandate\n\nFacilitate knowledge transfer between teams so that patterns proven in one program are reused in the others\n\nDomain Depth for This Posting\nAdaptive learning:\nModel each student’s current state: mastery, gaps and engagement\n\nSuggest the next best activity for every learner\n\nGenerate interactives across modalities: text, visual, audio, practice and simulation\n\nAdapt the full course path: remediate, re-sequence or accelerate\n\nGive teachers insight into strengths, weaknesses and where to intervene\n\nContent foundations:\nStructured, AI-ready curriculum content that can be authored, tagged, assembled and generated\n\nA learning object repository to find, reuse and fill gaps in existing resources\n\nData foundations: a large-scale student, curriculum and assessment data platform that supplies trusted learner signals to adaptive experiences\n\nSystems you will shape:\n\nLearner model service\n\nRecommendation and course adaptation engine\n\nMultimodal interactive generation services\n\nTeacher insights and intervention tools\n\nLearning object repository, content authoring and publishing services\n\nLearner data pipelines, APIs and the AI services that connect them\n\nPartner functions you cannot deliver without: Product Management, Learning Science, Design & Curriculum, Data Science & Data Engineering, Solution & Enterprise Architecture, Platform and Security Engineering\n\nPreferred Qualifications\n8-12 years of software engineering experience, including technical leadership of complex, cross-team initiatives\n\nHands-on experience building production applications with large language models and AI agents, including prompt and retrieval design, tool use and evaluation\n\nExperience designing data models and pipelines that turn learner or user behavior into signals a product can act on\n\nExperience with structured content models, content authoring tools or content delivery platforms\n\nStrong cloud-native engineering background (AWS preferred), with API-first and event-driven design\n\nBackground in education, teaching, learning design or ed-tech is a strong plus\n\nTrack record of turning a rough proof of concept into a durable platform that others build on\n\nRelevant degree in Computer Science or equivalent practical experience\n\nSkills and Experience\nThe skills and experience this role are assessed against.\nAdaptability\n\nArtificial Intelligence\n\nLearning to Learn\n\nEngineering fundamentals\n\nCollaboration\n\nInformation Security\n\nInnovation\n\nProblem Solving\n\nTechnical Acumen\n\nRole-specific skills\n\nAPI Management\n\nCloud Computing\n\nData Security\n\nDevOps\n\nEthical Responsibility\n\nResilience Planning\n\nSoftware Design\n\nEmerging skills\n\nAI Agents\n\nAutomated Artificial Intelligence (AutoAI)\n\nSpecialist depth for this role\n\nAdaptive learning design, including learner mastery modeling and personalized sequencing\n\nEvaluating AI-generated learning content and recommendations for accuracy, pedagogy and safety\n\nModeling structured curriculum content so it can be reused, assembled and generated\n\nCompensation at Pearson is influenced by factors including skill set, experience, and location.\nThe full-time salary range for this role is $155,000 - $175,000. This position is eligible to participate in an annual incentive program. Information on benefits can be found here.\nApplications will be accepted through 16th November 2026. 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