Member Of Technical Staff

Member of Technical Staff - Science I

San Francisco Bay Area | Bellevue, Washington | Hybrid | Department: Science

About AIVista

NTT DATA AIVista, Inc., a wholly owned subsidiary of NTT DATA, is based in Silicon Valley. We develop AI products for enterprises operating in complex regulatory environments. We work with NTT DATA Inc., NTT DATA Japan, and NTT DOCOMO to deploy systems for NTT clients. Our clients combine AIVista's product AI expertise with NTT DATA's industry knowledge and systems-integration experience.

The Role

The Member of Technical Staff Scientist advances AIVista's AI research agenda and turns research results into production systems. The work spans large language models, multimodal AI, intelligent agents, graph machine learning, safety, alignment, and evaluation. You will define research questions, develop algorithms and prototypes, design evaluations, and work with engineering to build reliable, scalable enterprise AI products.

The role also advances AIVista's work in ontologies and knowledge representation. You will develop methods that represent enterprise data, rules, permissible actions, and business processes as linked, versioned layers, then connect them to learning-based models and agents. This work draws on customer forms, systems of record (SORs), policies, standard operating procedures (SOPs), application programming interfaces, documents, and event logs. The resulting systems must preserve provenance, support human review, and

operate through governed interfaces.

Responsibilities

• Execute the long-term AI research roadmap aligned with company strategy, product priorities, and measurable business outcomes.

• Define and prioritize technical research bets across large language models (LLMs), multimodal AI, agents, graph machine learning, knowledge graphs, safety, neurosymbolic reasoning, alignment, and evaluation.

• Develop algorithms for ontology induction, schema matching, entity resolution, semantic alignment, consistency checking, and incremental ontology evolution.

• Develop methods that connect learning-based models with symbolic rules, formal constraints, and typed enterprise knowledge.

• Design ontology-grounded agents that reason over typed objects and act through governed, auditable interfaces.

• Create evaluations for induction precision and recall, alignment correctness, rule faithfulness, semantic consistency, determinism, calibration, latency, cost, and customer outcomes.

• Implement algorithms and architectures that create competitive advantage and move experimental models into scalable production systems.