Knowledge Graph & Ontology Lead

Company: r.Potential
Position: Knowledge Graph & Ontology Lead
Reports to: Head of Product
Location: San Francisco Bay Area (hybrid, 2–3 days per week in office)

About r.Potential

r.Potential exists to help people and companies realize their full potential as AI changes work. We're building an enterprise platform that helps leaders decide where to invest, what work AI can realistically take on, and how people's roles should evolve. We independently prove whether those changes deliver real business results. Drawing on a proprietary, global view of work across millions of companies, we're developing repeatable ways for people and AI to work together. Our ambition is for each company's decisions and results to improve the next company's choices. Join us to help shape how the next generation of companies works. r.Potential is based in SF and backed by Salesforce and The Adecco Group.

About the Opportunity

An org chart shows who reports to whom, but work moves through decisions, handoffs, dependencies, exceptions and informal coordination, and the evidence of it is scattered, incomplete, contradictory and constantly changing. Our Global Labor Graph connects that evidence across the global economy to reveal how work is organized and changing, then makes it specific to each enterprise: its value streams, workflows, tasks, decisions and human and AI capabilities. This role owns what the graph means and how good it is: what the evidence establishes, where we are making an inference, and how the picture changes as new evidence arrives or AI becomes capable of something new. Every product built on the graph depends on that model being one customers can trust.

We are looking for a Knowledge Graph & Ontology Lead. to design and evolve the semantic model at the center of our product. You will own the Global Labor Graph’s ontology, entity resolution and release quality and partner with data engineers, data scientists, Product and Engineering to turn fragmented evidence about work into a model that improves real enterprise decisions.

About the Frontier You’ll Help Shape

• Lead the design of an ontology that represents work coherently, connecting organizations, people, occupations, skills, tasks, decisions, AI agents, systems and outcomes, with the history and permissions each view of an enterprise needs.
• Turn fragmented evidence from external datasets and enterprise sources into one model: resolve companies and other entities, align taxonomies that disagree, reconstruct how work actually flows, and preserve provenance, uncertainty and conflicting claims.
• Make every graph release dependable, with versioned, tested builds and an evaluation suite that measures accuracy, unsupported relationships and reasoning correctness before anything ships, so corrections and new evidence make each release better than the last.
• Partner with Product and Engineering so recommendations, market intelligence and enterprise workcharts can use the graph and explain their results in its terms. If AI speeds up one activity, help a customer see whether the whole workflow improves or a bottleneck moves elsewhere.
• Test advances in knowledge representation, graph learning and language models against meaningful baselines, and decide what to adopt, adapt or set aside.
• Partner with Engineering, Security and business owners on access controls, licensing, derived-use rights and auditability, including the practices behind ISO 27001 and SOC 2.
• Lead others through initiatives: set technical direction, frame and review the work of engineers and scientists, and raise the team’s standards for modeling and evaluation.
  
  

About the Technology You’ll Build With

You will work with a large, varied data estate: more than a billion job postings spanning over 15 years, hundreds of millions of career profiles, tens of millions of company records, staffing demand and supply data from one of the world’s largest employers, and public occupational, skill and industry taxonomies. These sources use different vocabularies, identify companies differently and are revised continuously, so reconciling them is central to the role.

Our stack includes Neo4j, Databricks, Spark, Python, SQL, PostgreSQL and Microsoft Azure, with REST APIs and microservices for delivery. As a startup, we can evolve it where a new tool materially improves what we can build.

About Your Craft

• 8+ years of experience in data modeling, knowledge graphs or applied machine learning, with practical depth in ontologies, taxonomies or semantic graphs that you personally shaped and that powered real applications.
• Hands-on experience with entity resolution and taxonomy crosswalks across large, inconsistent sources, including how to measure and correct match quality.
• Strong hands-on ability in Python, SQL and Spark, with command of data engineering fundamentals: reliable pipelines, data quality, dataset and schema versioning, and production monitoring.
• Sound judgment in machine learning and statistical reasoning, enough to challenge a method’s assumptions, failure modes and evaluation, and to combine learned models with explicit structure where it helps.
• Systems thinking is grounded in detail: you can move between an enterprise’s business problem, its data, the representation underneath it and the behavior of an application, and you recognize when a local improvement damages the wider outcome.
• A record of turning difficult data or AI problems into products people use, and of explaining what you tested, what failed and how the evidence changed your design.
• Product judgment and clear communication: you can work with leadership, Product, Engineering and enterprise customers on what to build and why its conclusions deserve trust.
• Experience leading technical initiatives through influence: framing problems, guiding specialists and integrating their work into a coherent result.
  
  

About Your Edge

• Familiarity with occupational and skill taxonomies such as O*NET, SOC, ISCO and ESCO, or commercial job and skill taxonomies.
• Experience with graph databases such as Neo4j, and with RDF, OWL and SKOS or property-graph modeling.
• Experience with graph learning, LLM-based extraction, process intelligence or probabilistic reasoning.
• Experience in a startup or high-growth environment.
• A research background in knowledge representation or a related field; demonstrated technical ownership and product delivery count equally. We expect a deep foundation and the ability to learn across fields, not expertise in every technique.
  
  

About Working at r.Potential

This role is hybrid, with 2–3 days per week in our San Francisco office. You will start from a working graph, an established data team and direct partners in Product and Engineering, with room to shape the approach. Your first challenge is to show a useful result on a bounded enterprise problem, then build methods that carry across companies and improve with evidence.

r.Potential is part of The Adecco Group, is one of the world’s largest employers. We believe in talent, not labels, and focus on the diverse and unique skills our people bring. We have zero tolerance for any unlawful discrimination or harassment, against any employee, associate, or candidate, at any stage of that person’s journey with us. We seek to foster a culture of belonging and purpose, an environment where everyone can thrive and feel engaged, and where difference is respected and valued. Our commitment to equity, equal opportunity, inclusion, and diversity is part of our broader commitment to respecting fundamental human rights across our value chain. rPotential is proud to be an Equal Opportunity Employer. Equal Opportunity Employer/Veterans/Disabled. Click here to read our
Candidate Privacy Information Statement, which explains how we will use your information.

The Company will consider qualified applicants with arrest and conviction records in accordance with federal, state, and local laws and/or security clearance requirements, including, as applicable:

• The California Fair Chance Act
• Los Angeles City Fair Chance Ordinance
• Los Angeles County Fair Chance Ordinance

The anticipated annual base salary range for this position is between $230,000 - $285,000. Compensation varies based on a variety of factors including, but not limited to, experience, education, key skills, and geographic location.

Benefit offerings for full-time employment include medical, dental, vision, term life and AD&D insurance, short-term and long-term disability, additional voluntary benefits, commuter benefits, wellness plans, and a 401k plan or a non-qualified deferred compensation plan. Available paid leave includes Personal Time Off (PTO) on an accrual basis up to 152 hours a year, 10 Paid Holidays, 1 Community Service Day, and up to 6 weeks of Paid Parental Leave. PTO and holiday hours are prorated based on hire date within the calendar year.