Ndea Seeks Researchers for AGI Development Focused on Search Guidance

Ndea is actively recruiting for a pivotal role in its AGI systems development, emphasizing search guidance and deep learning.

Ndea is on the lookout for hands-on researchers and engineers to enhance its efforts in developing Artificial General Intelligence (AGI) systems, specifically through improved search guidance.

Role Overview

This full-time, remote position is designed for individuals eager to work at the forefront of neuro-symbolic AI. Candidates will be responsible for designing, implementing, and validating innovative approaches in code, contributing directly to the efficiency and reliability of structured search systems.

Compensation and Team Structure

Ndea offers a competitive salary, meaningful equity, and a robust benefits package. The company operates with a small, globally distributed team characterized by high talent density, and it emphasizes a culture of ownership and agency among its members.

Application Process

Interested candidates are encouraged to apply by sending an email to future@ndea.com. Applicants should include their location, examples of significant work in reinforcement learning or search guidance, relevant experience in symbolic systems, and links to professional profiles. A resume is optional if the provided links are sufficient.

Referral Program

Ndea also has a referral program in place, offering a $10,000 bonus to individuals who refer candidates that are successfully hired and remain with the company for at least 30 days.

Qualifications

Ideal candidates will possess strong hands-on experience in building and debugging reinforcement learning or search-driven systems, along with a track record of research contributions in these areas. They should be comfortable working independently in a small team and have an interest in symbolic approaches, including program synthesis and formal methods. Clear communication and documentation skills are essential for navigating complex research and engineering workflows.

While the role is tailored for those with a focus on search-guided learning, it may not suit individuals primarily engaged in LLM-only reinforcement learning or post-training pipelines.

This article was produced by NeonPulse.today using human and AI-assisted editorial processes, based on publicly available information. Content may be edited for clarity and style.

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