Founding AI Engineer (Knowledge Graphs & Applied AI)

OnHires•New York, NY

2d•Hybrid

About The Position

Relevant Recruiting is broken. Coaching is reserved for executives. Career transitions are often far more difficult than they should be. At Relevant, we're building a portfolio of products designed to help people navigate their careers with greater clarity, confidence, and support. Our products include Reva, Coach, Passport, and SuccessDNA — each tackling a different part of the career journey. This is not another productivity app or AI wrapper. We're building systems that help people understand opportunities, make better decisions, and navigate complex career paths. The problems we're solving sit at the intersection of search, knowledge representation, recommendations, reasoning, and human potential. We're still early. SuccessDNA is currently in design-partner and dogfooding stages, while several other products are being built from the ground up.

Requirements

7+ years of software engineering experience
Strong architectural and systems design skills
Experience building and operating production systems at scale
Ability to move from idea to implementation without waiting for detailed specifications
A pragmatic mindset focused on shipping, learning, and improving
Experience with graph databases
Experience with knowledge graphs
Experience with semantic modeling
Experience with entity resolution
Experience with search infrastructure
Experience with retrieval systems
Experience with ranking systems
Experience with recommendation systems
Experience with large-scale data platforms

Nice To Haves

Experience with LLM-powered products, agents, retrieval-augmented systems, or AI applications
Previous startup or founding engineer experience
Experience building products from 0→1
Experience leading engineers or technical initiatives
Background in talent, recruiting, HR technology, or career development
Interest in psychometrics, organizational design, or human performance

Responsibilities

Work across product, architecture, data, retrieval, search, and AI systems.
Operate in ambiguity, making architectural decisions, and building systems from first principles.
Create the technical roadmap.
Build graph-based retrieval systems.
Build semantic search and ranking infrastructure.
Build knowledge representation and entity relationships.
Build data models that support reasoning and personalization.
Build recommendation and matching systems.
Build agent-powered workflows and AI-enabled user experiences.
Build core platform capabilities used across multiple products.