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Data & AnalyticsHybrid

AI Scientist - Intern

NTT DATA AIVista · Hybrid

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Last seen by MeritLog September 12, 2026Source: AshbySource version: ashby-public-job-posting-v1

MeritLog read this listing from NTT DATA AIVista's Ashby job board and last checked it on September 12, 2026.

Source: the employer's Ashby job board. Open the original listing for current details.

Job details

Work model
Hybrid
Salary
$12,500 – $14,500 per month
Location
San Francisco Bay Area

Hiring context

How this role compares at NTT DATA AIVista

NTT DATA AIVista has 7 live roles in MeritLog’s catalog across 3 job families, and 2 of them are in data & analytics. 1 of those listings publish a pay range, a disclosure rate of 14%.

Counted across the job boards MeritLog tracks, at the time this page was served. Pay comparisons use only listings that publish a complete range in the same currency and period.

What the role asks for

What you'd do

  • Define research questions and develop algorithms, prototypes, and system designs across one or more focus areas.
  • Design rigorous experiments and benchmarks that measure correctness, robustness, calibration, privacy, latency, cost, and process outcomes.
  • Work with scientists, engineers, and domain experts to turn enterprise data, policies, feedback, and operational constraints into research artifacts and deployable systems.
  • Develop inspectable AI systems and analyses that connect model and agent decisions to
  • Move promising research toward production through simulation and controlled evaluation, and contribute results to high-quality research publications.

What they're asking for

  • Advanced PhD candidate in computer science, machine learning, artificial intelligence, or a related field.Education
  • A strong research record or demonstrated publication trajectory in relevant areas.Skill
  • Strong foundations in machine learning, algorithms, and statistical methods.Skill
  • Deep experience in at least one of the following: language models, knowledge representation or graphs, formal methods, agentic systems, continual learning, multimodal learning, or process mining.Skill
  • Proficiency in Python and experience designing and running rigorous empirical studies.Skill
  • Experience with neurosymbolic methods, autoformalization, formal verification, theoremSkillPreferred
  • Experience with ontology construction, knowledge graphs, entity resolution, graph learning, or graph-based retrieval.SkillPreferred
  • Experience with agent memory, context engineering, model routing or orchestration, planning, tool use, or multi-agent systems.SkillPreferred
  • Experience with continual, federated, or privacy-preserving learning; uncertainty calibration; human-in-the-loop systems; or regression-safe adaptation.SkillPreferred
  • Familiarity with process mining, digital twins, simulation, workflow systems, or graduated- autonomy deployments.SkillPreferred
  • Experience with robust and scalable benchmarking, agentic environment construction, and complex task metric design.SkillPreferred
  • Interest in bridging foundational research with deployed AI systems in regulated or high-stakes domains.SkillPreferred

Parsed by MeritLog from the employer’s own posting. The full description follows below.

Job description

AI Scientist Intern Palo Alto, California | 3-6 months About AIVista NTT DATA AIVista, Inc., a wholly owned subsidiary of NTT DATA, develops AI products for enterprises operating in complex regulatory environments. Based in Palo Alto, we combine deep AI product expertise with NTT DATA's industry knowledge and systems-integration experience, working with NTT companies to deploy solutions for enterprise clients. The Opportunity This research internship is designed for advanced PhD candidates who want to tackle foundational problems at the intersection of machine learning, knowledge representation, formal methods, and enterprise systems. You will work with scientists and engineers to turn research ideas into trustworthy AI capabilities evaluated on production-scale systems and data. The internship lasts 3-6 months, with the possibility of extension. Pursuing top-tier conference publications is highly encouraged, and projects are selected to support both rigorous research andpractical relevance. Science Focus • Neurosymbolic methods and trustworthy reasoning: Combine learning-based and symbolic techniques to improve reliability, transparency, and alignment with domain constraints. • Knowledge representation and semantic AI: Develop methods that help AI systems organize, connect, and reason over complex enterprise information. • Adaptive and model-agnostic AI systems: Explore flexible approaches that integrate models, tools, context, and memory across diverse tasks and environments. • Document and multimodal intelligence: Improve how AI systems understand and reason over information expressed across text, documents, images, and other modalities. • Learning from feedback: Develop methods that enable AI systems to improve safely and effectively from human and operational feedback. • Evaluation, verification, and interpretability: Advance rigorous approaches to assessing AI reliability, robustness, safety, and transparency. • AI for complex workflows: Explore how intelligent systems can support and improve multi-step enterprise processes while maintaining appropriate oversight and control. What You'll Do • Define research questions and develop algorithms, prototypes, and system designs across one or more focus areas. • Design rigorous experiments and benchmarks that measure correctness, robustness, calibration, privacy, latency, cost, and process outcomes. • Work with scientists, engineers, and domain experts to turn enterprise data, policies, feedback, and operational constraints into research artifacts and deployable systems. • Develop inspectable AI systems and analyses that connect model and agent decisions to evidence, rules, and outcomes. • Move promising research toward production through simulation and controlled evaluation, and contribute results to high-quality research publications. Qualifications • Advanced PhD candidate in computer science, machine learning, artificial intelligence, or a related field. • A strong research record or demonstrated publication trajectory in relevant areas. • Strong foundations in machine learning, algorithms, and statistical methods. • Deep experience in at least one of the following: language models, knowledge representation or graphs, formal methods, agentic systems, continual learning, multimodal learning, or process mining. • Proficiency in Python and experience designing and running rigorous empirical studies. Preferred • Experience with neurosymbolic methods, autoformalization, formal verification, theorem proving, or constraint solving. • Experience with ontology construction, knowledge graphs, entity resolution, graph learning, or graph-based retrieval. • Experience with agent memory, context engineering, model routing or orchestration, planning, tool use, or multi-agent systems. • Experience with continual, federated, or privacy-preserving learning; uncertainty calibration; human-in-the-loop systems; or regression-safe adaptation. • Familiarity with process mining, digital twins, simulation, workflow systems, or graduated- autonomy deployments. • Experience with robust and scalable benchmarking, agentic environment construction, and complex task metric design. • Interest in bridging foundational research with deployed AI systems in regulated or high-stakes domains. Compensation Details The monthly compensation range for this role is $12,500–$14,500. Individual compensation is determined based on factors including education, experience, skills, qualifications, geographic location, and business needs. Eligible interns may receive relocation and housing assistance. NTT DATA AIVista is an equal opportunity employer. We do not discriminate based on race, religion, color, national origin, ancestry, sex, gender identity or expression, sexual orientation, age, disability, genetic information, marital status, military or veteran status, reproductive health decisions, or any other characteristic protected under applicable federal, state, or local law.

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