Senior Engineer, Knowledge Systems

Justworks•New York, NY

9h•Onsite

About The Position

At Justworks, you’ll enjoy a welcoming and casual environment, great benefits, wellness program offerings, company retreats, and the ability to interact with and learn from leaders in the startup community. We work hard and care about our most prized asset - our people. We’re helping businesses get off the ground by enabling them to focus on running their business. We solve HR issues. We’re data-driven and never stop iterating. If you’d like to work in a supportive, entrepreneurial environment, are interested in building something meaningful and having fun while doing it, we’d love to hear from you. We're united by shared goals and shared motivations at Justworks. These are best summed up in our company values, which are reflected in our product and in our team. Our Values If this sounds like you, you’ll fit right in. Who You Are Not only do you have broad and deep knowledge of software development technologies, but you are also deeply curious about how knowledge flows through complex organizations. You enjoy designing systems that make information easier to structure, store, retrieve, and reuse across teams and tools. You have a strong sense of empathy for both technical and non-technical collaborators and are able to translate complex system designs into concepts that content designers, product managers, and business stakeholders can understand. You are particularly excited about the emerging intersection of AI systems, structured knowledge, and modern content infrastructure. You enjoy building foundational systems that enable others—engineers, designers, support teams, and AI tools—to work more effectively. In this role, you will partner closely with content strategists, designers, and product teams to build the infrastructure that powers Justworks’ internal knowledge systems and AI capabilities. This work includes designing systems that store structured knowledge, expose it through APIs, and enable downstream tools—including AI assistants—to retrieve reliable context.

Requirements

7+ years of experience as a software engineer building production systems
Strong experience designing APIs and backend services that support multiple downstream consumers
Experience working with structured content systems, CMS platforms, or knowledge management infrastructure
Experience leading the technical implementation of complex systems and coordinating work across multiple engineers, including delegating implementation tasks and guiding architectural decisions
Experience designing or integrating systems that support information retrieval or search
Familiarity with modern AI tooling and experience integrating LLM-based workflows with structured knowledge sources
Experience designing systems that model relationships between entities, policies, or domain concepts
Strong collaboration skills and ability to work closely with non-engineering partners such as content strategists, designers, and product managers
Ability to operate autonomously in ambiguous problem spaces and design pragmatic early-stage solutions

Nice To Haves

Headless CMS platforms (e.g., Contentful, Sanity, Strapi)
Git-based content workflows and markdown repositories
Knowledge retrieval systems and RAG architectures
Search and indexing systems
API-driven content platforms

Responsibilities

Design and build the foundational infrastructure that enables structured knowledge to be stored, versioned, and retrieved across the organization
Partner with Content Design, Product, and Engineering teams to translate knowledge architecture (taxonomy, content models, decision tables, and policy relationships) into technical systems
Stand up and configure a headless CMS platform that implements the organization’s knowledge operating model, translating content models, taxonomy, and governance structures defined by Content Design into scalable technical systems
Develop APIs and services that expose structured knowledge to internal tools, AI assistants, and future product experiences
Build and maintain integrations between knowledge storage systems and AI tooling, including retrieval pipelines and experimentation with RAG-based architectures
Prototype using lightweight tooling (e.g., Git-based content repositories, markdown workflows, and developer-friendly authoring environments)
Collaborate with engineers and designers to identify opportunities where structured knowledge can improve product development workflows and AI capabilities
Evaluate and recommend technology approaches for knowledge storage and delivery (e.g., headless CMS platforms, structured content repositories, search infrastructure)
Establish scalable patterns for content versioning, governance workflows, and structured metadata management
Work with cross-functional stakeholders to ensure the system supports internal enablement needs such as SOPs, policies, decision trees, and operational guidance
Continuously iterate on the architecture as the organization’s knowledge systems mature and new use cases emerge