Member of Technical Staff - Science I

NTT DATA AIVista•Palo Alto, CA

7d•Hybrid

About The Position

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 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.

Requirements

PhD in computer science, machine learning, artificial intelligence, mathematics, physics, or a related field, or equivalent industry impact.
At least 5 years of experience in artificial intelligence or machine learning, with significant experience applying science and research in production environments.
A proven record of impactful research demonstrated through publications, patents, or shipped AI products.
Strong foundations in machine learning, algorithms, statistical methods, experimental design, and scientific evaluation.
Deep experience in at least one relevant area: LLMs, agents, multimodal learning, graph machine learning, knowledge representation, formal methods, process mining, or AI evaluation.
Proficiency in Python and experience building research prototypes that engineering teams can transition to production.

Nice To Haves

Experience with ontology construction or evolution, knowledge graphs, entity resolution, schema matching, graph reasoning, or graph-based retrieval.
Experience with the Web Ontology Language (OWL), Resource Description Framework (RDF), description logics, semantic query languages, or ontology validation methods.
Experience with neurosymbolic methods, autoformalization, formal verification, theorem proving, satisfiability modulo theories (SMT), constraint solving, or policy engines.
Experience integrating enterprise data models, SOR schemas, application programming interfaces, and unstructured documents into common semantic representations.
Familiarity with process mining, Business Process Model and Notation (BPMN), workflow systems, simulation, or digital twins.
Experience designing evaluations where correctness, determinism, provenance, auditability, and safe failure behavior matter as much as model capability.
Experience deploying AI systems in regulated or high-stakes domains

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.
Partner with engineering to design model deployment, evaluation, monitoring, and continuous improvement loops.
Align scientific work with real customer problems and measurable operational outcomes.
Lead research publications, patents, and open-source contributions when strategically valuable.
Evaluate emerging techniques, tools, and vendor ecosystems, and represent AIVista externally through conferences, publications, and research partnerships.
Champion safe, ethical, and governed AI practices, and develop frameworks for model evaluation, bias mitigation, formal checks, and risk management.