Planned hiring
Software Engineer, Knowledge Graphs
Lucy is a proactive AI coach from Lucy Labs that lives on your desktop as a cloud-backed app.
Planned hiring. This description explains the work we are preparing to hire for; it is not a confirmed start date or offer.
Make Lucy's recommendations explainable, reusable and correctable. You will build the structured knowledge that lets Lucy connect a person's goal with relevant tasks, tools, skills and learning. A tool mention is easy to collect; knowing what it can reliably help someone accomplish takes engineering and careful judgment.
The work
Make Lucy's recommendations explainable, reusable and correctable. You will build the structured knowledge that lets Lucy connect a person's goal with relevant tasks, tools, skills and learning. A tool mention is easy to collect; knowing what it can reliably help someone accomplish takes engineering and careful judgment.
For example, researching meeting attendees and drafting an agenda may form a meeting-preparation workflow. You will represent those relationships without treating every observed click as proof of the user's intent. Your work makes recommendations explainable, reusable and correctable.
What you will own
- Implement versioned entities, relationships and queries for Personas, Goals, Tasks, Actions, Workflows, Solutions and their tool, skill and knowledge prerequisites.
- Turn participant-validated research about business processes, exceptions and vocabulary into the existing coachable concepts. Work with Product on ambiguous definitions and the acceptance criteria for a selected domain.
- Build identity resolution, deduplication, provenance and confidence handling for tool and technique knowledge arriving through the Tools and Techniques Repository. Preserve source evidence and make conflicting or stale claims visible to reviewers.
- Maintain mappings between Lucy's AI competencies and occupational reference knowledge, including O*NET work activities and skills. Document the limits of each crosswalk instead of assuming two taxonomies mean the same thing.
- Build semantic regression cases, migration checks and useful quality views. Test how schema or vocabulary changes affect coaching queries, score definitions and existing customer mappings.
- Keep customer-specific knowledge separate from reusable global knowledge, with explicit review and privacy requirements before promotion. The graph stores reusable entities; individual observations and user profiles have their own system owners.
Your partners
The Product Manager owns prioritization and semantic acceptance, informed by UX Research and field evidence. You own semantic implementation and its tests. Data Engineering owns ingestion transport, replay and storage lifecycle; AI Engineering implements extraction runtime. Applied Science proposes interpretations, and AI Evaluation independently tests their quality. You will work across these boundaries without needing a separate ontology department.
What you bring
Show us software you have built around structured knowledge, taxonomies or relationship-rich data. You should be comfortable with SQL, programming, schema evolution, APIs and reproducible tests, and able to explain why a relationship is supported by evidence. Experience resolving messy names or reconciling different vocabularies is valuable. We care about clear examples of working with domain experts and challenging weak assumptions more than a particular degree or graph database brand.
Your first 90 days
Agree one bounded coaching use case with Product and inspect its research examples and available data. Produce a reviewed vocabulary and representative queries, then implement a small end-to-end slice from validated source entities to a coaching query. Establish provenance and customer-boundary checks, a semantic regression set and a documented backlog of unresolved mappings. Success means another engineer can reproduce and safely extend the slice.
Next step
Express your interest
Email people@lucylabs.ai with the role title, a short introduction and relevant CV or portfolio. This is planned hiring; we will confirm current availability and the individual working arrangement.
Lucy Labs confirms the working arrangement, employer, location, eligibility, compensation and terms for each role. Our mailing address does not determine the job location or guarantee remote employment, worldwide eligibility or visa sponsorship.
Do not send passwords, government identifiers or bank details. Read the application privacy notice.
Frequently asked questions
Is this role open at Lucy Labs?
Lucy Labs lists this role as planned hiring. Availability depends on the need, team readiness and individual arrangement; it does not promise an immediate vacancy or start date.
Where does this work take place at Lucy Labs?
Lucy Labs confirms the working arrangement, employer, location, eligibility, compensation and terms for each role. Our mailing address does not determine the job location or guarantee remote employment, worldwide eligibility or visa sponsorship.
What does this role own at Lucy Labs?
Lucy Labs: Make Lucy's recommendations explainable, reusable and correctable. You will build the structured knowledge that lets Lucy connect a person's goal with relevant tasks, tools, skills and learning. A tool mention is easy to collect; knowing what it can reliably help someone accomplish takes engineering and careful judgment.
How do I express interest in Lucy Labs?
Email people@lucylabs.ai with the role title, a short introduction and relevant CV or portfolio. Lucy Labs uses the application to assess and respond to it; retention for future opportunities is a separate explained purpose and choice.
