Research Software Engineer

Job Title: Research Software Engineer - Data Fabric & Ontology

Location

STIC (Software Technology Innovation Center), Sunnyvale, CA, USA

About STIC & the Foundations Lab

STIC is SLB's Silicon Valley applied research center - the company's bleeding-edge evangelists. We operate deliberately outside the critical path of engineering delivery, which gives us the freedom to pursue the high-risk, high-reward research that teams under delivery pressure cannot. Our mandate is to identify frontier digital technology before it goes mainstream, translate it into business advantage for SLB, and embed it through partnership and knowledge transfer.

The Foundations Lab owns STIC's Data Fabric & Ontology theme - how SLB architects, harmonizes, and reasons over data at industrial scale. This is not maintenance engineering. We are building toward a multi-year arc: from AI-driven semantic harmonization of messy legacy data today, to living knowledge graphs that mirror business logic and physical assets, to self-evolving data architectures that rewrite their own schemas as new use cases emerge.

The Role

We're hiring a Research Software Engineer to help lead that arc. You'll design and build proof-of-concept systems that de-risk transformative ideas in data fabric, ontology engineering, knowledge graphs, and agentic data management - and translate them into a clear point of view on what SLB should adopt next.

This is a hands-on, high-visibility role. You'll own solutions from inception to delivery, work directly with Big Tech and the Silicon Valley startup ecosystem, and shape the technical direction of one of STIC's six strategic themes. Reporting to the Foundations Lab manager, you'll be expected to lead through the work - building the systems, forming the opinion, and bringing others along.

We're looking for a self-starter who thrives in ambiguity, treats experimentation as the default, and wants to define what industrial data platforms look like in 2030 rather than iterate on what they look like today.

What You'll Work On

• Semantic harmonization & AI-ready data - AI-driven \"semantic mappers\" that harmonize fragmented legacy data into standard schemas; ontologies, knowledge graphs, and data virtualization for industrial-scale operations.
• Ontology engineering & knowledge graphs - AI-assisted and AI-constructed ontologies, GraphRAG and multi-hop reasoning, and knowledge graphs as living digital twins of business logic linked to physical assets.
• Data agents & agentic data management - Autonomous agents for data discovery, integration, quality, and governance; the execution layer of a modern data fabric.
• Knowledge runtime & the truth layer - Systems that manage retrieval, verification, reasoning, access control, and audit - including shared human-AI knowledge systems and in-context capture of tacit expertise.
• Platform & system design - Scalable, fault-tolerant, cloud-native architectures (Kubernetes, GCP/Azure/AWS) and modern data infrastructure (lakehouse, open table formats, streaming) - in service of the research above.

Responsibilities

• Identify - Track the frontier of data fabric, ontology, and knowledge infrastructure; spot genuinely groundbreaking technology before it becomes mainstream.
• Translate - Build proof-of-concept systems that de-risk ideas, then form a clear, evidence-based opinion on what SLB should adopt, watch, or ignore.
• Embed - Partner with other STIC labs, SLB business units, and the external ecosystem to transfer knowledge and catalyze adoption of validated technology.
• Shape technical direction for the Data Fabric & Ontology theme - generating ideas, defining new research directions, and owning projects end to end.
• Collaborate with AI engineers and researchers to integrate ML and agentic capabilities into the broader data landscape.
• Establish thought leadership - disseminating knowledge through talks, tutorials, and internal briefings across SLB.
• Provide technical mentorship and raise the bar for engineering craft across the lab.

Qualifications

• MS or PhD in Computer Science, Data/Information Science, or a related field - or equivalent demonstrated depth.
• 8+ years building production-grade data, distributed, or ML systems, with deep expertise in several of: data platforms and pipelines, knowledge graphs / graph databases, semantic technologies and ontologies, LLM/RAG and agentic systems, or cloud-native distributed systems.
• A track record of taking ambiguous, open-ended problems from concept to working system.
• Strong written and verbal communication - you can turn a technical result into a decision-useful point of view.

Strongly preferred

• Hands-on experience with ontology engineering, knowledge graphs, GraphRAG, or semantic layers.
• Experience with agentic systems, LLM orchestration, or AI memory / knowledge-runtime architectures.
• Familiarity with modern data infrastructure (lakehouse, open table formats such as Iceberg/Delta, streaming with Kafka/Spark).
• Exposure to industrial, energy, or other complex real-world data domains.

What sets a candidate apart

• Evidence of frontier awareness - you already have opinions on grounding AI reasoning in business context, AI-constructed ontologies, or where AI knowledge-management is heading.
• A builder's instinct paired with a researcher's rigor - you prototype fast but reason carefully about what the result actually proves.

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