Artificial Intelligence Research Scientist - Python

In this role, you will be responsible for designing architectures for meta-learning, self-reflective agents, and recursive optimization loops. You will also build simulation frameworks grounded in Bayesian dynamics, attractor theory, and teleo-dynamics. Moreover, your duties will include developing systems integrating graph rewriting, knowledge representation, and neurosymbolic reasoning. Additionally, you will conduct research on fractal intelligence structures, swarm-based agent coordination, and autopoietic systems. Your role will involve advancing Mobius's knowledge graph with ontologies supporting logic, agency, and emergent semantics. Furthermore, you will integrate logic into distributed, policy-scoped decision graphs aligned with business and ethical constraints. As part of your responsibilities, you will publish cutting-edge results and mentor contributors in reflective system design and emergent AI theory. Lastly, you will build scalable simulations of multi-agent, goal-directed, and adaptive ecosystems within the Mobius runtime. Qualifications Required: - Ph.D. or M.Tech in Artificial Intelligence, Cognitive Science, Complex Systems, Applied Mathematics, or equivalent experience. Proven Expertise In: - Meta-learning, recursive architectures, and AI safety - Distributed systems, multi-agent environments, and decentralized coordination - Formal and theoretical foundations, including Bayesian modeling, graph theory, and logical inference - Strong implementation skills in Python (required), with additional proficiency in C++, functional or symbolic languages being a plus - Publication record in areas intersecting AI research, complexity science, and/or emergent systems Preferred Qualifications: - Experience with neurosymbolic architectures and hybrid AI systems - Familiarity with fractal modeling, attractor theory, and complex adaptive dynamics - Knowledge of topos theory, category theory, and logic-based semantics - Exposure to knowledge ontologies, OWL/RDF, and semantic reasoners - Understanding of autopoiesis, teleo-dynamics, and biologically inspired system design - Knowledge in swarm intelligence, self-organizing behavior, and emergent coordination - Experience with distributed learning systems like Ray, Spark, MPI, or agent-based simulators Technical Proficiency: - Programming Languages: Python (required), C++, Haskell, Lisp, or Prolog (preferred for symbolic reasoning) - Frameworks: PyTorch, TensorFlow - Distributed Systems: Ray, Apache Spark, Dask, Kubernetes - Knowledge Technologies: Neo4j, RDF, OWL, SPARQL - Experiment Management: MLflow, Weights & Biases - GPU and HPC Systems: CUDA, NCCL, Slurm - Formal Modeling Tools: Z3, TLA+, Coq, Isabelle In summary, you will play a crucial role in designing cutting-edge architectures, conducting research, and advancing Mobius's knowledge graph within a dynamic and innovative environment.,