NTT Data Aivista

NTT Data Aivista

Member of Technical Staff - Science I

San Francisco Bay Area · Staff+

Sponsorship not specified$300k-$400kDetected 48 days ago
PythonAlgorithmsMachine LearningLLMsResearchExperimental Design

Stay score

odds of building a lasting career here

59Sponsors, lottery-bound
Cap-exempt (no lottery)0
Sponsors this role80
Entry-level history0
PERM / green-card track0
Lottery odds (Level IV)94
Fits your clock70

Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.

Lottery odds assume a STEM candidate.

Personalize to your clock →

Employer immigration record

from this employer's Department of Labor filings

Files H-1B transfers

8 transfer filings in the last year, covering 8 workers. Median labor-condition decision: 7 days. An employer that already files transfers is one that can take over an existing H-1B.

Sourced from Department of Labor LCA, PERM and prevailing-wage disclosure data. Employer matching is by name, so figures may be split across an employer's legal entities. Absence of a filing means none appears in our copy of the data, not that none exists.

Community outcomes

No reports yet — be the first to help the next applicant.

About 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.
  • The role also advances AIVista's work in ontologies and knowledge representation.

Responsibilities

  • Develop algorithms for ontology induction, schema matching, entity resolution, semantic alignment, consistency checking, and incremental ontology evolution.
  • 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.
  • Lead research publications, patents, and open-source contributions when strategically valuable.
  • Champion safe, ethical, and governed AI practices, and develop frameworks for model evaluation, bias mitigation, formal checks, and risk management.

Requirements

  • A proven record of impactful research demonstrated through publications, patents, or shipped AI products.
  • 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.
  • 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.
  • Preferred
  • Experience integrating enterprise data models, SOR schemas, application programming interfaces, and unstructured documents into common semantic representations.

Nice to have

  • Proficiency in Python and experience building research prototypes that engineering teams can transition to production.
  • 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.
  • 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

Compensation

  • The estimated base salary range for this role is $300,000-$400,000 USD.

Benefits

  • Medical, dental, and vision insurance
  • Significant Company HSA contribution
  • Paid holidays and flexible PTO
  • The salary for the successful applicant will depend on job-related factors such as education, training, work experience, business needs, and market demands.
  • Total compensation also includes variable pay in the form of an annual target bonus and other cash incentives, with a combined potential of up to 100% of base salary.
  • 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 methods that connect learning-based models with symbolic rules, formal constraints, and typed enterprise knowledge.

Equal opportunity

  • equal opportunity employer.
Apply directly at NTT Data Aivista →Create a free account for alerts like thisView NTT Data Aivista immigration profile

This listing is sourced directly from NTT Data Aivista's careers page and normalized into a canonical job model.