Job Description
At Nexus Horizon, we are not just predicting the future; we are architecting it. We are looking for a visionary Lead AI Research Engineer to spearhead our initiatives in Artificial General Intelligence (AGI) and next-generation autonomous systems. In this role, you will bridge the gap between theoretical machine learning breakthroughs and scalable production environments.
We are seeking a pioneer who is obsessed with pushing the boundaries of what is possible in 2026 and beyond. You will work alongside world-class physicists, cryptographers, and software engineers to build the foundational models that will define the next decade of human-machine interaction.
Why Join Nexus Horizon?
- Work on mission-critical projects that impact billions.
- Access to the latest H100 clusters and quantum-accelerated hardware.
- Competitive equity package and fully remote-first culture.
- Professional development budget unlimited.
Responsibilities
- Lead the research and development of novel deep learning architectures targeting AGI capabilities, including reasoning, planning, and multimodal understanding.
- Design scalable pipelines for training, fine-tuning, and evaluating large language models and multimodal agents.
- Collaborate with the engineering team to translate research prototypes into robust, production-grade APIs and services.
- Conduct rigorous experimentation, including A/B testing and bias detection, to ensure model fairness and safety.
- Publish high-impact research papers at top-tier conferences (NeurIPS, ICML, ICLR) to establish industry leadership.
- Mentor junior researchers and data scientists, fostering a culture of innovation and continuous learning.
- Stay abreast of the rapidly evolving AI landscape to identify emerging trends and integrate them into our roadmap.
Qualifications
- Ph.D. or M.S. in Computer Science, Mathematics, Physics, or a related field with a focus on AI/ML.
- 5+ years of experience in research, with a proven track record of leading complex projects from conception to deployment.
- Expert proficiency in Python, PyTorch, or TensorFlow.
- Strong theoretical foundation in optimization, linear algebra, and probability theory.
- Experience with large-scale distributed training frameworks (Ray, Kubernetes, Slurm).
- Demonstrated ability to publish in peer-reviewed venues or open-source communities.
- Excellent communication skills, with the ability to explain complex technical concepts to diverse stakeholders.