Job Description
We are pioneering the next generation of Artificial Intelligence and are seeking a visionary Senior AI Engineer to join our team. As we accelerate toward the AI Renaissance of 2026, we are building autonomous agents and self-improving systems that will redefine human-machine interaction.
In this pivotal role, you will not just be using existing models; you will architect the foundational systems that power the future. You will work on cutting-edge projects involving large language models (LLMs), reinforcement learning, and multimodal AI architectures.
Why Nexus Horizon Labs?
We offer a competitive salary, equity packages, and the opportunity to work on problems that matter. Our culture is built on innovation, speed, and ethical AI development.
Responsibilities
- Architect Advanced AI Systems: Design and implement scalable machine learning pipelines and autonomous agent frameworks for the 2026 technology stack.
- Optimize Model Performance: Fine-tune and distill large language models (LLMs) to reduce inference latency while maximizing accuracy and coherence.
- Develop Multimodal Interfaces: Create systems that seamlessly integrate text, vision, and audio to deliver intuitive user experiences.
- Research & Experimentation: Stay ahead of the curve by researching novel techniques in generative AI, prompt engineering, and reasoning models.
- Collaborate Cross-Functionally: Partner with product managers, designers, and backend engineers to translate complex AI capabilities into user-facing products.
- Ethical AI Governance: Implement guardrails and safety protocols to ensure AI outputs are fair, unbiased, and secure.
Qualifications
- Education: Master’s or PhD in Computer Science, Mathematics, or a related technical field (or equivalent professional experience).
- Experience: 5+ years of professional experience in machine learning, deep learning, or AI research.
- Technical Skills: Proficiency in Python, PyTorch, TensorFlow, or JAX. Strong understanding of neural network architectures, specifically Transformers and RNNs.
- LLM Expertise: Demonstrated experience working with LLMs, fine-tuning techniques (LoRA, QLoRA), and RAG (Retrieval-Augmented Generation) architectures.
- Problem Solving: Ability to tackle ambiguous problems and devise innovative solutions in a fast-paced environment.
- Communication: Excellent written and verbal communication skills to present technical concepts to non-technical stakeholders.