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About the role
As a Senior Applied Scientist at Accrete AI, you will play a crucial role in advancing agentic AI systems for decision automation, knowledge gathering, and organizational intelligence. Your innovative research will focus on designing and prototyping next-generation systems that excel in adaptive, long-horizon decision-making. Your contributions will be pivotal in building knowledge engines that unify structured and unstructured data, capturing tacit organizational knowledge for autonomous and semi-autonomous agents operating at an enterprise scale. Key Responsibilities: - Conduct forward-looking applied research supporting decision automation, knowledge gathering, and structured reasoning over complex real-world data. - Contribute to the design and evolution of knowledge-centric representations, including graph-based and relational structures, to support intelligent systems. - Explore and develop agent-based approaches for reasoning, planning, and adaptation across extended tasks and dynamic environments. - Develop and evaluate semantic, relational, and causal representations enabling explainable, trustworthy AI-driven decision-making. - Study methods for integrating learning, memory, and context into AI systems, including approaches for capturing and leveraging tacit knowledge. - Collaborate closely with engineering teams to translate research ideas into scalable prototypes and production-ready systems. - Participate in the evaluation and benchmarking of models, systems, and architectures focusing on reliability, robustness, and reasoning quality. - Contribute to the broader research direction through mentorship, publications, and intellectual property development. Required Qualifications: - Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Cognitive Systems, or a closely related field; or Masters degree with 2+ years of experience leading applied research or product-focused AI systems. - Experience applying machine learning to real-world problems; experience with enterprise data or decision-support systems is a plus. - Strong foundation in knowledge representation and relational reasoning, including graph-based approaches. - Expertise in deep learning architectures, including Transformers, Graph Neural Networks (GNNs), and Large Language Models (LLMs). - Demonstrated ability to build and leverage knowledge graphs or structured knowledge systems for machine learning and reasoning applications. - Knowledge in causal inference, probabilistic reasoning, or decision modeling is highly desirable. - Hands-on experience with LLM prompting, fine-tuning, and agent-oriented application development. - Strong programming skills with experience developing agentic AI systems, including LLM orchestration, and programmatic reasoning workflows. - Experience deploying deep learning and LLM-based systems in cloud environments (e.g., AWS, Azure), with familiarity in modern inference frameworks. - Experience with graph databases, large-scale graph processing, and graph libraries (e.g., NetworkX, iGraph, Graph-tool). - Strong problem-solving skills and the ability to work independently and collaboratively in cross-functional teams. - Excellent communication skills, with the ability to clearly articulate complex technical concepts. - A publication record in AI, knowledge representation, agent systems, network science, or related fields is highly desirable. As a Senior Applied Scientist at Accrete AI, you will play a crucial role in advancing agentic AI systems for decision automation, knowledge gathering, and organizational intelligence. Your innovative research will focus on designing and prototyping next-generation systems that excel in adaptive, long-horizon decision-making. Your contributions will be pivotal in building knowledge engines that unify structured and unstructured data, capturing tacit organizational knowledge for autonomous and semi-autonomous agents operating at an enterprise scale. Key Responsibilities: - Conduct forward-looking applied research supporting decision automation, knowledge gathering, and structured reasoning over complex real-world data. - Contribute to the design and evolution of knowledge-centric representations, including graph-based and relational structures, to support intelligent systems. - Explore and develop agent-based approaches for reasoning, planning, and adaptation across extended tasks and dynamic environments. - Develop and evaluate semantic, relational, and causal representations enabling explainable, trustworthy AI-driven decision-making. - Study methods for integrating learning, memory, and context into AI systems, including approaches for capturing and leveraging tacit knowledge. - Collaborate closely with engineering teams to translate research ideas into scalable prototypes and production-ready systems. - Participate in the evaluation and benchmarking of models, systems, and architectures focusing on reliability, robustness, a