AGI Model -ML DL Researcher

Role Summary

As an AGI Research Engineer at EVEOAI, you will research, design, develop, and experiment with advanced learning architectures and intelligent systems focused on the scientific and engineering challenges of Artificial General Intelligence (AGI). You will explore the foundations of general intelligence, including learning, reasoning, abstraction, planning, memory, knowledge transfer, adaptation, decision-making, and autonomous problem-solving.

You will work across the complete AGI research lifecycle—from mathematical foundations, computational models of intelligence, and experimental hypothesis development to novel learning algorithms, cognitive architectures, world models, reinforcement learning, continual learning, meta-learning, and autonomous agent systems. Your work will focus on understanding how intelligent systems can acquire generalizable knowledge, learn new skills with limited experience, transfer capabilities across domains, and solve previously unseen problems.

You will investigate approaches inspired by machine intelligence, cognitive science, computational neuroscience, information theory, evolutionary computation, and decision theory to understand and develop systems with increasingly general problem-solving capabilities. You will design experiments, reproduce research findings, develop novel architectures, establish evaluation benchmarks, and investigate the limitations of current AI systems.

Your work will contribute to EVEOAI's long-term vision of developing general-purpose intelligent systems capable of learning, reasoning, adapting, planning, and solving diverse problems across unfamiliar environments. This role emphasizes fundamental research, scientific experimentation, mathematical modeling, and the development of new approaches toward AGI rather than conventional task-specific AI applications.

Core Responsibilities

• Research and develop computational architectures and learning algorithms aimed at advancing Artificial General Intelligence (AGI), generalization, adaptive intelligence, and autonomous problem-solving.

• Investigate the theoretical foundations of intelligence, including learning, reasoning, abstraction, planning, memory, knowledge representation, decision-making, and cognitive architectures.

• Formulate research questions, develop testable hypotheses, design controlled experiments, analyze results, and identify new directions for AGI research.

• Study and implement mathematical foundations of intelligence, including Linear Algebra, Probability, Statistics, Calculus, Optimization, Information Theory, Decision Theory, Game Theory, and Computational Learning Theory.

• Explore learning paradigms such as self-supervised learning, unsupervised learning, reinforcement learning, meta-learning, transfer learning, continual learning, active learning, and evolutionary learning.

• Investigate how intelligent systems can generalize beyond training distributions, transfer knowledge between unrelated tasks, learn from limited examples, and adapt to unfamiliar environments.

• Research reasoning, abstraction, compositionality, causal inference, analogical reasoning, logical inference, hypothesis generation, and multi-step problem-solving.

• Design and experiment with cognitive architectures that integrate perception, memory, reasoning, planning, learning, action selection, and feedback.

• Investigate world models, predictive learning, environment modeling, causal representations, and model-based decision-making to improve the ability of intelligent systems to understand and predict environmental dynamics.

• Research memory architectures, including working memory, episodic memory, semantic memory, procedural memory, associative memory, and mechanisms for persistent knowledge retention.

• Develop and evaluate continual and lifelong learning methods that enable systems to acquire new knowledge and capabilities while minimizing catastrophic forgetting.

• Explore meta-learning and learning-to-learn approaches that allow intelligent systems to adapt quickly to new tasks, environments, and problem domains.

• Investigate reinforcement learning, hierarchical reinforcement learning, model-based reinforcement learning, reward design, exploration strategies, and long-horizon decision-making.

• Research planning, search algorithms, hierarchical task decomposition, goal-directed behavior, and decision-making under uncertainty.

• Explore neuro-symbolic AI, symbolic reasoning, knowledge graphs, program synthesis, automated theorem proving, and hybrid approaches combining neural computation with formal reasoning.

• Investigate evolutionary computation, genetic algorithms, neuroevolution, population-based learning, and open-ended learning as potential approaches to developing increasingly capable intelligent systems.

• Study computational neuroscience and cognitive science to investigate mechanisms of learning, abstraction, memory consolidation, attention, prediction, and adaptive behavior.

• Research the relationship between intelligence, information processing, compression, representation learning, predictive coding, and efficient knowledge acquisition.

• Develop experimental systems capable of learning through interaction, observation, feedback, simulation, and accumulated experience.

• Investigate autonomous learning systems that can identify knowledge gaps, generate learning objectives, select informative experiences, evaluate outcomes, and improve their capabilities over time.

• Explore multi-agent intelligence, cooperative learning, communication, distributed cognition, emergent behavior, and collective problem-solving.

• Design experiments for evaluating general intelligence capabilities, including cross-domain transfer, sample efficiency, reasoning consistency, adaptability, compositional generalization, causal understanding, and out-of-distribution performance.

• Develop research benchmarks, experimental environments, synthetic task distributions, simulation frameworks, and evaluation protocols for measuring progress toward general-purpose intelligence.

• Investigate the limitations of existing intelligent systems, including brittle generalization, shortcut learning, catastrophic forgetting, reward hacking, distribution sensitivity, and failures in unfamiliar environments.

• Research AGI safety, alignment, interpretability, uncertainty estimation, robustness, controllability, and mechanisms for reliable behavior in complex environments.

• Conduct systematic literature reviews, analyze research papers, reproduce published results, implement experimental baselines, and compare alternative scientific approaches.

• Develop novel algorithms, mathematical formulations, computational models, and experimental architectures based on identified gaps in existing AGI research.

• Apply rigorous experimental methodologies, including ablation studies, statistical significance testing, sensitivity analysis, controlled comparisons, and reproducibility practices.

• Investigate the computational efficiency of general learning systems, including sample efficiency, algorithmic efficiency, memory requirements, compute scaling, and data efficiency.

• Collaborate with AI researchers, mathematicians, software engineers, and interdisciplinary research teams to translate theoretical insights into experimental intelligent systems.

• Document research findings, communicate technical insights, contribute to open-source research where appropriate, and pursue opportunities for research publications and scientific collaboration.

• Continuously evaluate emerging research in AGI, cognitive architectures, general-purpose learning, computational neuroscience, and autonomous intelligence to identify promising research directions for EVEOAI.

Requirements

• BTech/MTech/MSc/PhD (or equivalent) in Computer Science, Artificial Intelligence, Machine Learning, Mathematics, Statistics, Computational Neuroscience, Robotics, Physics, or a related quantitative field.

• Strong Python programming skills and practical knowledge of NumPy, SciPy, Pandas, and scientific computing tools.

• Strong mathematical foundation in Linear Algebra, Probability, Statistics, Calculus, Optimization, Information Theory, Discrete Mathematics, and Mathematical Logic.

• Strong understanding of Machine Learning, Deep Learning, statistical learning theory, representation learning, optimization algorithms, and generalization.

• Practical experience with PyTorch, JAX, TensorFlow, or similar frameworks for implementing and experimenting with learning algorithms.

• Understanding of learning theory, including inductive bias, sample complexity, generalization bounds, overfitting, representation learning, and out-of-distribution generalization.

• Knowledge of reinforcement learning, Markov Decision Processes (MDPs), partially observable decision processes, reward functions, value functions, policy optimization, and exploration strategies.

• Understanding of meta-learning, transfer learning, continual learning, active learning, self-supervised learning, and learning-to-learn methodologies.

• Strong interest in AGI research, general intelligence, cognitive architectures, computational models of intelligence, or autonomous learning systems.

• Understanding of reasoning and planning approaches, including search algorithms, symbolic logic, causal reasoning, hierarchical planning, and decision-making under uncertainty.

• Familiarity with world models, model-based learning, predictive representations, environment simulation, or causal inference.

• Knowledge of memory systems, knowledge representation, associative learning, information retrieval, and mechanisms for retaining and transferring knowledge.

• Familiarity with neuro-symbolic AI, evolutionary computation, program synthesis, automated reasoning, or other approaches to general-purpose intelligence.

• Ability to read, understand, critically evaluate, and implement methods described in academic research papers.

• Experience reproducing published research, conducting controlled experiments, implementing algorithms from scratch, or developing novel research prototypes.

• Understanding of experimental design, hypothesis testing, statistical significance, ablation studies, benchmarking, and reproducible scientific research.

• Experience with simulation environments, reinforcement learning environments, synthetic data generation, or interactive learning systems is beneficial.

• Familiarity with computational neuroscience, cognitive science, information theory, complex systems, or evolutionary biology is beneficial.

• Knowledge of GPU computing, parallel computing, distributed experimentation, and performance optimization is beneficial.

• Understanding of Git, Linux, experiment tracking, scientific documentation, and reproducible development workflows.

• Strong analytical thinking, mathematical reasoning, intellectual curiosity, and the ability to investigate complex problems without predefined solutions.

• Ability to distinguish empirical evidence from theoretical assumptions, critically analyze research limitations, and communicate findings clearly.

• A research-oriented mindset with an interest in exploring open-ended scientific questions, developing original ideas, and contributing to long-term progress toward AGI.

what you will be learn beyond the works

• Advanced experience with AGI research methodologies, computational theories of intelligence, general-purpose learning architectures, and cognitive computing.

• Experience studying and implementing meta-learning, few-shot learning, zero-shot generalization, transfer learning, and continual learning for adaptive intelligence.

• Experience with advanced reinforcement learning, including model-based reinforcement learning, hierarchical reinforcement learning, inverse reinforcement learning, exploration strategies, and long-horizon planning.

• Experience developing world models, predictive environments, causal representations, simulation-based learning, and model-based decision-making systems.

• Experience designing cognitive architectures that integrate memory, reasoning, perception, planning, learning, and action selection.

• Experience with episodic memory, semantic memory, working memory, memory consolidation, associative learning, and persistent knowledge architectures.

• Exposure to computational neuroscience and cognitive science research on learning, attention, abstraction, reasoning, prediction, and memory formation.

• Experience with neuro-symbolic AI, formal logic, automated theorem proving, program synthesis, symbolic planning, and hybrid reasoning architectures.

• Experience with evolutionary computation, genetic programming, neuroevolution, population-based optimization, and open-ended learning systems.

• Exposure to active learning, curiosity-driven learning, intrinsic motivation, information-seeking behavior, and autonomous curriculum generation.

• Experience developing systems that learn from interaction, identify their own knowledge gaps, select informative experiences, and adapt their behavior through feedback.

• Experience with multi-agent learning, emergent coordination, collective intelligence, distributed problem-solving, and cooperative decision-making.

• Experience designing novel research benchmarks for measuring generalization, abstraction, compositional reasoning, transfer efficiency, and adaptive intelligence.

• Exposure to theoretical research involving computational complexity, algorithmic information theory, Bayesian inference, information compression, and the mathematical foundations of intelligence.

• Experience investigating causal discovery, counterfactual reasoning, analogical transfer, compositional generalization, and systematic problem-solving.

• Experience with AGI safety, alignment, interpretability, robustness, uncertainty estimation, and evaluation of increasingly capable autonomous systems.

• Experience with high-performance scientific computing, GPU acceleration, distributed experimentation, parallel algorithms, and scalable research infrastructure.

• Opportunities to reproduce influential research papers, identify unresolved research problems, develop original algorithms, and contribute to scientific publications.

• Opportunities to collaborate across disciplines, including artificial intelligence, mathematics, neuroscience, cognitive science, robotics, physics, and complex systems research.

• Experience translating theoretical ideas into experimental prototypes, evaluating hypotheses through empirical evidence, and iteratively improving research approaches.

• Opportunity to contribute to EVEOAI's long-term research direction toward general-purpose intelligence capable of learning, reasoning, adapting, and solving unfamiliar problems across diverse domains.