🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
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 Our research agenda spans three horizons. You'll contribute across the near term while helping us reach toward the frontier: 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 Depending on level and specialization, you'll: 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 Required 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. ALL APPLICANTS FOR U.S. ROLES MUST CAREFULLY READ ALL THE SUPPLEMENTAL U.S. SPECIFIC INFORMATION LISTED BELOW. SLB is committed to a culture where everyone feels like they belong. To learn more about our diversity, equity, and inclusion commitments, please visit our Diversity & Inclusion | SLB (slb.com) page for more information. When certifying your application, you are also certifying on reading and understanding all of the below supplemental information. For purposes of the application and the below information, Company shall be defined to mean SLB. Please note that the below supplemental information and the employment application are NOT a contract. EQUAL EMPLOYMENT OPPORTUNITY & VETERANS Company policy is to provide every individual a fair and equal opportunity to seek employment and advancement at the Company without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, citizenship, genetic information, veteran or military status, disability, creed, ancestry, pregnancy (including pregnancy, childbirth and related medical conditions), marital status or any factors protected by federal, state, or local laws. We are an "Equal Opportunity Employer". For more information please, refer to the latest version of "Know Your Rights" poster and the "Pay Transparency Nondiscrimination Poster" located here: https://www.dol.gov/agencies/ofccp/posters. The Company is a VEVRAA Federal Contractor - priority referral Protected Veterans requested. REASONABLE ACCOMMODATIONS The Company will endeavor to make a reasonable accommodation / modification to the known physical or mental limitations of a qualified applicant with a disability to assist in the hiring process, unless the accommodation would impose an undue hardship on the operation of our business, in accordance with applicable federal, state, and local law. If you believe you require such assistance to complete the application or to participate in the interview process, please contact accommodationhotline@slb.com to request assistance. Please note that only those inquiries concerning a request for reasonable accommodation will be responded to. AUTHORIZATION FOR EMPLOYMENT Pursuant to the Immigration Reform and Control Act of 1986, all applicants who are offered and accept employment must produce documents establishing their identity and authorization for employment
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.