AI Scientist - Intern

San Francisco Bay Area · RemoteinternshipRemote

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entry

$13k–$15k

H1B: yes

Ntt Data Aivista AI Scientist Intern at Ntt Data Aivista focused on research at the intersection of machine learning, knowledge representation, and enterprise systems. Develop trustworthy AI capabilities evaluated on production-scale data.

Responsibilities

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.
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.
Define research questions and develop algorithms, prototypes, and system designs across one or more focus areas.

Qualification

Strong foundations in machine learningExperience with neurosymbolic methodsprovingExperience with ontology constructionExperience with agent memoryExperience with continualFamiliarity with process mining

Required

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.