Business Services & Consulting • all cities, NY 35
Research Scientist/Engineer (35)
all cities, NY 35On-sitePosted 6 hours ago
Business Services & Consulting
About the Role
Position Summary
We are seeking brilliant, unconventional thinkers to join us. In this role, you will not be chasing incremental gains on standard benchmarks. Instead, you will be tasked with developing the foundational principles of Artificial General Intelligence. We believe that understanding the "code of the brain" is the most viable path to building truly intelligent machines, and we are looking for scientists who can bridge the gap between biological intelligence, computational theory, and building at scale.
Core Responsibilities
Fundamental research on brain-like world models: Develop and implement new architectures and learning algorithms for neuroscience-based world models. You will draw up on insights from deep learning, causality, probabilistic and energy based models, and biologically inspired architectures.
Scientific Discovery: Design experiments to test hypotheses regarding high-level cognition, causal representations, sensory-motor integration, active inference, and unsupervised learning.
Cross-Disciplinary Collaboration: Work alongside neuroscientists and software engineers to translate abstract mathematical frameworks into scalable systems.
Contribute to open science: Help innovate new publication models that incentivize speedy dissemination, open source code releases, free open access, and impact measurements based on uptake.
Desired Qualifications
Technical Depth: Preferred: PhD in Computer Science, Electrical Engineering, Neuroscience, Physics, or a related quantitative field. We are always open to considering exceptional candidates, even those with non-traditional educational histories.
Theoretical Rigor: Strong foundations in deep learning, graphical models, and information theory.
Coding Proficiency: Expert-level skills in deep learning frameworks (PyTorch/JAX), with a focus on clean, reproducible research code.
AGI Mindset: A demonstrated interest in the "big questions" of AI—robustness, generalization, and common-sense reasoning.
Curious about the brain: A genuine passion for exploring the computational and architectural principles of the mammalian brain.
Startup DNA: Ability to thrive in a lean, fast-paced environment where you have high autonomy and a direct influence on research direction.
Why Join Us?
We recognize the need for fundamental research: No pressure to align research with quarterly revenue goals, market competition, or product roadmaps. We can focus on solving the core problems and achieving breakthroughs that enable the next generation of AGI technology.
Mission-First: Your work contributes directly to the global pool of knowledge and the safe development of AGI.
Collaborative culture: We work in teams, organized around challenge problems and attack paths, without big-company inertia or politics.
Position Summary
We are seeking brilliant, unconventional thinkers to join us. In this role, you will not be chasing incremental gains on standard benchmarks. Instead, you will be tasked with developing the foundational principles of Artificial General Intelligence. We believe that understanding the "code of the brain" is the most viable path to building truly intelligent machines, and we are looking for scientists who can bridge the gap between biological intelligence, computational theory, and building at scale.
Core Responsibilities
Fundamental research on brain-like world models: Develop and implement new architectures and learning algorithms for neuroscience-based world models. You will draw up on insights from deep learning, causality, probabilistic and energy based models, and biologically inspired architectures.
Scientific Discovery: Design experiments to test hypotheses regarding high-level cognition, causal representations, sensory-motor integration, active inference, and unsupervised learning.
Cross-Disciplinary Collaboration: Work alongside neuroscientists and software engineers to translate abstract mathematical frameworks into scalable systems.
Contribute to open science: Help innovate new publication models that incentivize speedy dissemination, open source code releases, free open access, and impact measurements based on uptake.
Desired Qualifications
Technical Depth: Preferred: PhD in Computer Science, Electrical Engineering, Neuroscience, Physics, or a related quantitative field. We are always open to considering exceptional candidates, even those with non-traditional educational histories.
Theoretical Rigor: Strong foundations in deep learning, graphical models, and information theory.
Coding Proficiency: Expert-level skills in deep learning frameworks (PyTorch/JAX), with a focus on clean, reproducible research code.
AGI Mindset: A demonstrated interest in the "big questions" of AI—robustness, generalization, and common-sense reasoning.
Curious about the brain: A genuine passion for exploring the computational and architectural principles of the mammalian brain.
Startup DNA: Ability to thrive in a lean, fast-paced environment where you have high autonomy and a direct influence on research direction.
Why Join Us?
We recognize the need for fundamental research: No pressure to align research with quarterly revenue goals, market competition, or product roadmaps. We can focus on solving the core problems and achieving breakthroughs that enable the next generation of AGI technology.
Mission-First: Your work contributes directly to the global pool of knowledge and the safe development of AGI.
Collaborative culture: We work in teams, organized around challenge problems and attack paths, without big-company inertia or politics.
What You'll Do
Fundamental research on brain-like world models: Develop and implement new architectures and learning algorithms for neuroscience-based world models. You will draw up on insights from deep learning, causality, probabilistic and energy based models, and biologically inspired architectures.
Scientific Discovery: Design experiments to test hypotheses regarding high-level cognition, causal representations, sensory-motor integration, active inference, and unsupervised learning.
Cross-Disciplinary Collaboration: Work alongside neuroscientists and software engineers to translate abstract mathematical frameworks into scalable systems.
Contribute to open science: Help innovate new publication models that incentivize speedy dissemination, open source code releases, free open access, and impact measurements based on uptake.
Technical Depth: Preferred: PhD in Computer Science, Electrical Engineering, Neuroscience, Physics, or a related quantitative field. We are always open to considering exceptional candidates, even those with non-traditional educational histories.
Theoretical Rigor: Strong foundations in deep learning, graphical models, and information theory.
