Job Description
Are you ready to architect the technological reality of 2026? Nexus Horizon is seeking a visionary Senior AI & Robotics Engineer to lead the development of next-generation autonomous systems. We are building the infrastructure for tomorrow, today.
In this pivotal role, you will be at the forefront of innovation, designing scalable neural networks and integrating advanced robotics into our core product suite. You will work in a high-performance, collaborative environment where your code will directly impact the future of human-machine interaction.
Join us and help us bridge the gap between current capabilities and the 2026 technological horizon.
Responsibilities
- Architect and deploy advanced AI models, focusing on large language models and reinforcement learning for 2026-ready scalability.
- Lead technical research initiatives to explore breakthroughs in edge computing and autonomous decision-making.
- Collaborate with hardware teams to optimize software for next-gen robotic processors and sensor fusion.
- Mentor junior engineers, fostering a culture of technical excellence, innovation, and continuous learning.
- Define and implement rigorous testing protocols to ensure system reliability, safety, and performance.
- Drive product strategy by translating complex research into actionable product features that solve real-world problems.
Qualifications
- Education: Master’s or PhD in Computer Science, Robotics, AI, or a related quantitative field from a top-tier institution.
- Experience: 5+ years of experience in AI/ML development, with a strong portfolio of published research or deployed products.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, and C++.
- Domain Knowledge: Deep understanding of natural language processing (NLP), computer vision, or robotic control systems.
- Soft Skills: Exceptional problem-solving abilities and the ability to communicate complex technical concepts to non-technical stakeholders.
- Future-Proof Mindset: A passion for emerging technologies and a proactive approach to learning new paradigms.