How does swipeandhired work?

You set your target roles, salary floor and locations once. swipeandhired scores matching postings against your profile, drafts tailored application materials with AI, and either lets you swipe to apply, one tap at a time, or runs fully on autopilot. Every application is tracked in your dashboard.

Which ATS platforms does swipeandhired support?

All of them. swipeandhired applies on every applicant tracking system (ATS) employers use, including Workday, SmartRecruiters, Greenhouse, iCIMS, Lever, Ashby, Taleo, SuccessFactors, Oracle Cloud and Jobvite, and on a company's own careers page when it uses none of them. You can also bring any posting yourself by link or pasted text.

Can I cancel my subscription?

Anytime, in one click from the billing portal in Settings. Your plan stays active until the end of the current billing period. No retention calls, no dark patterns.

Is my data used for training AI models?

No. Your resume and application data are processed only to run the service for you, matching, drafting, and submitting applications. We never sell your data. If you bring your own AI provider key, requests go directly to your provider under their terms.

Lead Specialist, AI Scientist at Pearson, Remote

Lead Specialist, AI Scientist at Pearson, remote. Full-time. $140,000 to $160,000 a year. Apply with swipeandhired.

We are seeking a Lead Specialist, AI Data & Knowledge Architect , to design and build the knowledge foundation of Pearson’s next generation of intelligent K–12 assessment and learning systems.
This role sits within the Measurement & Learning team and operates at the intersection of Content Development, Learning Science, Technology, AI Science, and Psychometrics, with responsibility for translating deep assessment and measurement expertise into extensible computational representations. Prior assessment experience is helpful but not required.
This work will build a deep, layered knowledge architecture of interconnected graphs beneath our assessment authoring system. It will ground the system's AI agents and intelligent workflows, and support both formative and summative assessment. Standards, competencies, claims, evidence, task designs, scoring, and content will be represented as connected, machine-readable knowledge that AI, measurement, and content workflows can reason over.
This is a founding, hands-on role for someone eager to innovate at the edge of current practice: building what does not yet exist, testing it rigorously, and adapting quickly. The architecture will support predictive, statistical, graph-based, deterministic, and generative AI methods alike.
Knowledge Architecture & Modeling

Design and build the knowledge graph and underlying knowledge model for our assessment and learning domains, selecting among ontologies, schemas, vocabularies, and taxonomies as the problem requires
Represent standards, competencies, skills, claims, evidence, task characteristics, misconceptions, content, and their relationships as computationally usable structures
Design for extensibility across content areas, programs, assessment contexts, and future AI capabilities, starting simple without closing off future growth
Establish approaches for stable identity, versioning, provenance, lineage, and lifecycle status of knowledge objects
Clearly separate authoritative knowledge, expert judgments, derived information, and machine-generated hypotheses

Graph & AI Data Architecture

Evaluate and select graph and semantic technologies based on extensibility, interoperability, reasoning needs, maintainability, and downstream AI use
Build the pipelines, mappings, and synchronization processes that connect item banks, standards, metadata, psychometric data, and other sources into a coherent knowledge layer
Develop the schemas, APIs, data contracts, and services through which applications, agents, and analytic workflows access the knowledge architecture
Partner with software engineers to integrate the knowledge layer into our assessment authoring system

Knowledge-Grounded AI, Retrieval & Context

Design retrieval and context architectures that let AI and machine-learning systems use structured knowledge effectively, including graph, semantic, vector, and hybrid retrieval
Create task-specific knowledge projections so each capability receives what it needs without complex relationships being flattened into unstructured prompts
Maintain traceability between AI outputs and the knowledge used to produce them
Work with Applied AI Systems Engineers to ensure generative, predictive, agentic, and analytic capabilities operate reliably over the shared knowledge layer

Innovation & Technical Judgment

Exercise a high degree of autonomy in shaping architecture where no established blueprint exists
Prototype quickly, test assumptions empirically, and change course when evidence warrants
Evaluate emerging approaches, including graph-based, knowledge-grounded, and neuro-symbolic methods, and adopt those that show measurable value
Make architectural decisions, assumptions, and tradeoffs explicit and open to challenge from colleagues

Responsible AI, Quality & Evaluation

Design knowledge systems that make AI reasoning transparent, auditable, and traceable to its sources
Support AI that strengthens human expertise rather than replacing it, with clear points for expert review and judgment
Partner with AI engineers, psychometricians, and content experts to test whether knowledge and retrieval designs measurably improve accuracy and reliability
Establish quality, coverage, and consistency measures for knowledge assets
Uphold responsible data practices, including privacy, security, access control, and appropriate use of student and content data

Cross-Functional Collaboration

Partner with Technology teams and software engineers responsible for the assessment authoring system
Co-design architecture with AI Science colleagues and Applied AI Systems Engineers, providing and receiving technical challenge
Partner with psychometricians and learning scientists to represent measurement and learning concepts faithfully
Work with content development teams and subject-matter experts to capture domain knowledge, terminology, relationships, and constraints
Communicate complex architectural concepts clearly to technical and non-technical audiences

Required Qualifications

Advanced degree in Computer Science, Artificial Intelligence, Information Science, Knowledge Engineering, or a related field, or equivalent experience
Demonstrated experience designing and building knowledge graphs in production or substantial applied settings
Hands-on experience with knowledge modeling and semantic schema design, using approaches such as ontologies, property-graph schemas, vocabularies, and taxonomies
Strong command of graph and semantic technologies (e.g., RDF, OWL, SHACL, SPARQL, property graphs) and their architectural tradeoffs
Strong data architecture skills, including data modeling, integration pipelines, identity, versioning, provenance, and lineage
Strong programming ability in Python or comparable languages, with a record of building working systems and APIs
Deep understanding of predictive and generative AI, including how retrieval-grounded and agentic systems use structured knowledge
Track record of building novel systems where no established solution existed, with sound judgment and high autonomy
Ability to translate expertise from other disciplines into computational representations and to communicate tradeoffs clearly .

Preferred Qualifications

Strong understanding of how educational knowledge is structured, through K–12 standards, competency frameworks, or skills-based architectures such as skills taxonomies and credential frameworks, ideally from modeling them as structured data
Experience with knowledge graphs that power applications such as content generation, framework alignment, personalized guidance, or analytic insights
Experience with formal ontology engineering, including OWL-based modeling, reasoning, or validation
Experience building hybrid AI systems that combine machine-learned or generative models with structured knowledge, rules, or constraints, including neuro-symbolic approaches
Experience with graph machine learning, graph embeddings, link prediction, or graph neural networks
Experience in domains where outputs must be validated against reliable evidence or expert judgment, such as scientific, healthcare, financial, or legal AI
Experience with evaluation datasets, human annotation and adjudication workflows, or benchmark development
Familiarity with educational measurement, psychometrics, principled assessment design, or assessment content systems
Publications, patents, or open-source contributions in knowledge representation or graph technologies

Design Philosophy

Knowledge as a durable, extensible organizational asset
Build to learn: prototype, measure, iterate
Innovation at the edge of current practice, grounded in evidence
Transparent, auditable, and traceable AI reasoning
AI that supports rather than supplants human expertise
The right method for the problem, not the newest one

Why This Role Matters
This role lays the foundation for an industry-defining assessment system. By making expert knowledge explicit, extensible, and computationally usable, and by inventing new approaches where none exist, this architect will enable more accurate, transparent, and trustworthy generation, alignment, verification, analysis, and guidance across formative and summative assessment.
Compensation at Pearson is influenced by factors including skill set, experience, and location.
The full-time salary range for this role is $140,000 – $160,000. This position is eligible to participate in an annual incentive program. Information on benefits can be found here .
Applications will be accepted through 20th November 2026. This window may be extended depending on business needs.
Apply today and imagine the impact you can make.

#LI-P1
#LI-Remote