Job description

Postdoctoral AI Scientist Palo Alto, California | 12-24Month Appointment 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 AIVista is seeking a Postdoctoral AI Scientist to conduct research on foundational problems at the intersection of machine learning, knowledge representation, formal methods, and AI agents for enterprise systems. You will work alongside scientists and engineers to define and pursue ambitious research questions, develop new methods and systems, and evaluate them against complex, production-scale problems and data. You will conduct research that advances the state of the art while creating a path toward performant and trustworthy AI systems that can operate reliably in real enterprise environments. This is an an initial 12-month appointment with the option to extend it to 24 months postdoctoral research appointment designed for researchers who have completed their PhD and want to continue developing a strong research agenda while working closely with real-world AI systems. Pursuing publication of research at top-tier conferences and journals is highly encouraged, and projects are selected to support both rigorous scientific contribution and practical 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. Scalable domain model development: Develop scalable methods for building domain-specialized models, spanning automated generation of training and agentic environments, mid-training on domain data, post-training through fine-tuning and reinforcement learning, and rigorous domain-grounded evaluation. - 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 and independently drive research questions across one or more of AIVista's science focus areas. - Develop novel algorithms, models, methods, prototypes, and system designs and evaluate their effectiveness through rigorous experimentation. - Design experiments and benchmarks that measure correctness, robustness, calibration, privacy, latency, cost, and process outcomes. - Work with scientists, engineers, and domain experts to translate enterprise data, policies, feedback, and operational constraints into research problems, 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 from early experimentation through simulation, controlled evaluation, and potential application in production-scale AI systems. - Communicate research findings internally and externally and contribute high-quality research to leading conferences, journals, and the broader scientific community. - Collaborate with and contribute to AIVista's broader research agenda while developing an independent body of research during the postdoctoral appointment. Qualifications - PhD in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Applied Mathematics, Electrical or Computer Engineering, Operations Research, or a closely related technical field. The PhD must be completed prior to the start of the appointment. - Demonstrated research record in machine learning, artificial intelligence, computer science, or a related technical discipline, evidenced through peer-reviewed publications, significant research projects, or comparable scientific contributions. - Strong foundations in machine learning, algorithms, statistical methods, experimental design, and empirical research. - Deep research experience in one or more of the following areas: large language models, knowledge representation or knowledge graphs, formal methods, agentic systems, continual learning, multimodal learning, evaluation and interpretability, or process mining. - Ability to independently formulate research questions, design rigorous experiments, analyze results, and communicate scientific findings. - Proficiency in Python and experience implementing and evaluating machine learning or AI systems. - Strong written and verbal communication skills and the ability to collaborate effectively across research, engineering, and domain teams. Areas of Additional Research Experience Relevant research experience may include: - Neurosymbolic methods, autoformalization, formal verification, theorem proving, or constraint solving. - Ontology construction, knowledge graphs, entity resolution, graph learning, or graph-based retrieval. - Mid-training (continued pretraining), supervised fine-tuning, preference optimization, or reinforcement learning for building domain-specialized models. - Synthetic data and task generation, verifiable reward design, or scalable pipelines for training and evaluating domain models. - Agent memory, context engineering, model routing and orchestration, planning, tool use, or multi-agent systems. - Continual, federated, or privacy-preserving learning; uncertainty calibration; human-in-the-loop systems; or regression-safe adaptation. - Process mining, digital twins, simulation, workflow systems, or graduated-autonomy deployments. - Robust and scalable benchmarking, agentic environment construction, and complex task metric design. - Research involving AI systems deployed in regulated, safety-sensitive, or other high-stakes environments. Compensation & Benefits The estimated annual base salary for this role is $150,000USD. Individual compensation is determined based on job-related factors including education, research experience, skills, qualifications, geographic location, business needs, and market conditions. Eligible employees may participate in Company-sponsored benefits in accordance with applicable Company policies. Housing and relocation assistance may be available based on individual circumstances and business needs. Eligible postdoctoral researchers may also receive travel assistance associated with relocation to and from the Palo Alto area, in accordance with Company policy. 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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Postdoctoral AI Scientist
Palo Alto, California | 12-24Month Appointment
AIVista is seeking a Postdoctoral AI Scientist to conduct research on foundational problems at the intersection of machine learning, knowledge representation, formal methods, and AI agents for enterprise systems.
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.
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 and independently drive research questions across one or more of AIVista's science focus areas.
Develop novel algorithms, models, methods, prototypes, and system designs and evaluate their effectiveness through rigorous experimentation.
Design experiments and benchmarks that measure correctness, robustness, calibration, privacy, latency, cost, and process outcomes.
Work with scientists, engineers, and domain experts to translate enterprise data, policies, feedback, and operational constraints into research problems, 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 from early experimentation through simulation, controlled evaluation, and potential application in production-scale AI systems.
Communicate research findings internally and externally and contribute high-quality research to leading conferences, journals, and the broader scientific community.
Collaborate with and contribute to AIVista's broader research agenda while developing an independent body of research during the postdoctoral appointment.
Strong foundations in machine learning, algorithms, statistical methods, experimental design, and empirical research.
Ability to independently formulate research questions, design rigorous experiments, analyze results, and communicate scientific findings.
Proficiency in Python and experience implementing and evaluating machine learning or AI systems.
Strong written and verbal communication skills and the ability to collaborate effectively across research, engineering, and domain teams.
Areas of Additional Research Experience
Neurosymbolic methods, autoformalization, formal verification, theorem proving, or constraint solving.

Nice-to-haves

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aimachine learningpython

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