AI Scientist - Intern
$150,000–$174,000 year
RemoteUnited States
Job Summary
Define research questions and develop algorithms, prototypes, and system designs across neurosymbolic methods, knowledge representation, and adaptive AI systems. Design rigorous experiments and benchmarks measuring correctness, robustness, calibration, and latency while working with scientists and engineers to turn enterprise data into deployable systems. Develop inspectable AI analyses connecting model decisions to evidence and rules, then move promising research toward production through simulation and controlled evaluation. Contribute results to high-quality publications, with a focus on trustworthy reasoning and complex enterprise workflows. This 3-6 month internship supports foundational research at the intersection of machine learning and formal methods for NTT DATA AIVista in Palo Alto.
Required 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
Desired Qualifications
- 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
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