Job Description
Join the Architects of 2026
At Nexus Horizon Systems, we aren't just building software; we are engineering the infrastructure for the future. We are looking for a visionary Lead AI Research Engineer to define the technological roadmap for 2026 and beyond. You will work at the intersection of Generative AI, Long-term Forecasting Models, and Autonomous Systems. If you are passionate about solving unsolved problems and pushing the boundaries of what is possible in artificial intelligence, this is your stage.
Why Join Us?
- Work on high-impact projects that define the next decade of tech.
- Competitive equity and salary packages.
- Access to state-of-the-art computing infrastructure.
- Collaborative environment with top-tier minds in Silicon Valley.
Responsibilities
- Define the 2026 Vision: Lead the strategic research and development of advanced AI architectures aimed at solving complex, long-term technical challenges.
- Model Architecture Design: Design, train, and optimize large-scale neural networks, focusing on efficiency, scalability, and alignment.
- Technical Leadership: Mentor a team of junior researchers and engineers, fostering a culture of innovation and scientific rigor.
- Cross-Functional Collaboration: Partner with product and engineering teams to translate theoretical research into practical, deployable applications.
- R&D Publication: Publish high-impact research papers in top-tier conferences (NeurIPS, ICML, ICLR) and contribute to the open-source community.
- Ethical AI Governance: Ensure all models adhere to strict ethical guidelines and safety protocols for future deployment.
Qualifications
- Education: Ph.D. or Master’s degree in Computer Science, Mathematics, Physics, or a related technical field with a focus on AI/ML.
- Experience: 5+ years of experience in machine learning research or software engineering, with a proven track record of leading technical projects.
- Technical Stack: Proficiency in Python, PyTorch, TensorFlow, and experience with distributed computing frameworks (Ray, Kubernetes).
- Specialized Knowledge: Deep understanding of Deep Learning, Natural Language Processing (NLP), or Computer Vision. Familiarity with Reinforcement Learning and LLM fine-tuning is highly preferred.
- Research Mindset: Demonstrated ability to conduct independent research, hypothesize, experiment, and iterate rapidly.
- Communication: Excellent verbal and written communication skills, with the ability to explain complex technical concepts to diverse stakeholders.