Accrete

Accrete AI is a dynamic and innovative company focused on transforming the future of artificial intelligence. We specialize in creating advanced AI solutions that turn complex data into actionable insights, driving real-world impact for businesses and government organizations. Our team thrives on creativity and collaboration, working together to push the boundaries of AI technology. At the core of our offerings are AI agents—autonomous systems that analyze multimodal data, generate insights, and make intelligent recommendations. These agents help businesses streamline operations, improve decision-making, and empower government entities to enhance security, intelligence, and operational efficiency.

We are seeking a highly motivated and innovative Senior Applied Scientist to join our research team, focused on advancing agentic AI systems for decision automation, knowledge gathering, and organizational intelligence. In this role, you will work at the intersection of AI agents, large language models, knowledge graphs, and causal reasoning to design and prototype next-generation systems that move beyond search and static analytics toward adaptive, long-horizon decision-making agents. Your work will contribute to building knowledge engines; dynamic, evolving systems that unify structured and unstructured data, capture tacit organizational knowledge, and provide grounded context for autonomous and semi-autonomous agents operating at enterprise scale.

• →Conduct forward-looking applied research that supports 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 that enable 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, with attention to reliability, robustness, and reasoning quality.
• →Contribute to the broader research direction through mentorship, publications, and intellectual property development.

• Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Cognitive Systems, or a closely related field; or Master’s 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.