Job Description
We are building the technological backbone for the year 2026. At Horizon Core, we don't just predict the future; we engineer it. We are seeking a visionary 2026 Readiness AI Architect to lead our research into Agentic AI, Autonomous Systems, and next-generation Neural Interfaces.
In this pivotal role, you will bridge the gap between theoretical AI research and scalable production infrastructure. You will be responsible for designing systems that are not only powerful today but are future-proofed to handle the computational complexity and ethical demands of the mid-2020s.
Why join Horizon Core?
- Future-First Vision: Work on cutting-edge projects that define the AI roadmap for 2026 and beyond.
- Elite Engineering Culture: Collaborate with world-class researchers and engineers in a premium, innovation-driven environment.
- Competitive Compensation: A salary and equity package that reflects your expertise in high-demand futuristic technologies.
If you are a technologist who looks past the horizon and builds what comes next, we want to hear from you.
Responsibilities
- Architect Future-Proof Systems: Design and implement scalable AI infrastructures capable of handling the exponential growth of data and model complexity expected in 2026.
- Lead Agentic AI Development: Spearhead the research and deployment of autonomous agents capable of complex reasoning and multi-step planning.
- Optimize Neural Architectures: Continuously refine model efficiency, focusing on edge-computing capabilities and low-latency inference for next-gen devices.
- Ethical AI Governance: Establish frameworks for AI safety, bias mitigation, and transparency to ensure responsible innovation.
- Cross-Functional Leadership: Mentor a team of ML engineers and data scientists, fostering a culture of technical excellence and forward-thinking.
- Strategic Roadmapping: Define the technical roadmap for Horizon Core, ensuring alignment with emerging industry standards and regulatory requirements.
Qualifications
- Advanced Education: MS or PhD in Computer Science, Mathematics, Physics, or a related technical field.
- Technical Mastery: Extensive experience with Deep Learning frameworks (PyTorch, TensorFlow, JAX) and distributed computing systems (Kubernetes, Ray).
- 2026-Ready Expertise: Demonstrated experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and Reinforcement Learning from Human Feedback (RLHF).
- System Design: Proven ability to design end-to-end machine learning pipelines, from data ingestion to model deployment and monitoring.
- Problem Solving: Exceptional analytical skills with a track record of solving complex, unstructured problems in ambiguous environments.
- Communication: Ability to translate complex technical concepts into clear strategies for non-technical stakeholders.