ABOUT ASTERA:

Astera is a private foundation on a mission to steer science and technology toward an abundant future for all. We believe the coming years will bring an era of unprecedented scientific and technological advancement as exponential progress in AI converges with central advances in other fields to dramatically accelerate innovation. This inflection point provides an unparalleled opportunity to fundamentally rethink the institutions, systems, and tools that drive scientific progress. We are searching for visionary leaders who are compelled by the challenge of driving transformative change in the way science is funded, conducted, and communicated.
Obelisk is the AGI research lab at Astera. Obelisk is a team of researchers who are pursuing an exploratory, neuroscience-informed approach to engineering AGI.
Astera enables the Obelisk team to focus on basic research and take a long-term view. Obelisk is unconstrained by the need to secure funding, garner profit, or publish results. The team also has access to significant computational resources. You can read more about Obelisk here.

POSITION SUMMARY:

We are seeking AI Researchers with a strong background in Reinforcement Learning (RL), autonomous agents, and adaptive learning systems. You will lead the design and execution of experiments that explore:

• Architectures capable of self-improvement over long time horizons.
• Novel RL formulations for open-ended skill acquisition.
• Agent-based systems that dynamically adapt their policies, goals, and internal structures.

Your work will combine theory, algorithm development, and hands-on system building, culminating in working prototypes that advance our understanding of AGI.

QUALIFICATIONS:

• Education: PhD in Computer Science, Machine Learning, AI, Neuroscience, or a closely related field
• Expertise: Deep knowledge of RL (e.g., model-based RL, meta-RL, hierarchical RL) and experience in designing novel algorithms
• Skills: Proficiency in Python and ML frameworks (PyTorch, JAX, TensorFlow)
• Mindset: Passion for open-ended research, curiosity-driven experimentation, and building systems from first principles
• Bonus: Experience with self-play, multi-agent systems, evolutionary algorithms, or life-long learning systems

• Conduct original research in reinforcement learning and adaptive systems
• Design and implement experiments to test new algorithms and architectures
• Develop agents capable of continual self-improvement without explicit retraining
• Publish results in top AI/ML venues and share insights with the broader research community
• Collaborate with an interdisciplinary team of AI researchers, engineers, and cognitive scientists