AI Scientist - Intern

NTT DATA AIVista•Palo Alto, CA

8d•Onsite

About The Position

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 and practical relevance.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.