Research Software Engineer

SLB•Sunnyvale, CA

3d•Onsite

About The Position

STIC (Software Technology Innovation Center) is SLB's applied research center focused on frontier digital technology. The Foundations Lab within STIC focuses on Data Fabric & Ontology, aiming to architect, harmonize, and reason over industrial-scale data. This involves building towards multi-year goals such as AI-driven semantic harmonization of legacy data, living knowledge graphs, and self-evolving data architectures. The Research Software Engineer role is a hands-on, high-visibility position responsible for designing and building proof-of-concept systems in data fabric, ontology engineering, knowledge graphs, and agentic data management. The role involves working with Big Tech and the Silicon Valley startup ecosystem, shaping technical direction, and leading through work. The ideal candidate is a self-starter who thrives in ambiguity and focuses on defining future industrial data platforms.

Requirements

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.
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 skills to translate technical results into decision-useful points of view.

Nice To Haves

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.
Evidence of frontier awareness regarding grounding AI reasoning in business context, AI-constructed ontologies, or the future of AI knowledge management.
A builder's instinct paired with a researcher's rigor.

Responsibilities

Identify the frontier of data fabric, ontology, and knowledge infrastructure and spot groundbreaking technology before it becomes mainstream.
Build proof-of-concept systems to de-risk ideas and form clear, evidence-based opinions on technology adoption.
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 research directions, and owning projects end to end.
Collaborate with AI engineers and researchers to integrate ML and agentic capabilities into the data landscape.
Establish thought leadership through talks, tutorials, and internal briefings.
Provide technical mentorship and raise the bar for engineering craft across the lab.