Job Description
Are you ready to define the intelligence of tomorrow?
Nexus Horizon Labs is at the forefront of the 2026 revolution, building the next generation of Autonomous General Intelligence (AGI). We are seeking a visionary AGI Research Engineer to join our elite R&D division and architect systems that bridge the gap between current Large Language Models and sentient reasoning.
In this pivotal role, you will not just use AI; you will teach it. You will develop novel training methodologies, evaluate safety and alignment, and deploy models that solve complex, unsolved problems in real-world environments. Join us in creating the infrastructure that powers the future of humanity.
Why Nexus Horizon?
- Work on Year 2026 timeline projects with real-world impact.
- Competitive salary, equity, and comprehensive benefits.
- Access to cutting-edge compute clusters and research grants.
- A culture of radical transparency and innovation.
Responsibilities
- Architect and train next-generation reasoning models focused on the 2026 landscape, improving on current benchmarks like MMLU and GSM8K.
- Design novel training pipelines that enhance model interpretability and reduce hallucination rates.
- Collaborate with cross-functional teams to integrate AGI models into scalable software ecosystems.
- Evaluate model safety, ethics, and alignment using rigorous adversarial testing frameworks.
- Conduct theoretical research to advance the field of deep learning and reinforcement learning.
- Publish high-impact research papers and contribute to open-source communities.
Qualifications
- PhD or Master’s degree in Computer Science, Artificial Intelligence, or a related technical field.
- 5+ years of professional experience in Machine Learning research or applied AI engineering.
- Strong proficiency in Python, PyTorch, or TensorFlow.
- Proven track record of publishing research in top-tier conferences (NeurIPS, ICML, ICLR).
- Deep understanding of NLP, Transformer architectures, and distributed systems.
- Experience with model quantization and optimization for edge deployment.